期刊文献+
共找到500篇文章
< 1 2 25 >
每页显示 20 50 100
Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets
1
作者 Shuo Xu Yuefu Zhang +1 位作者 Xin An Sainan Pi 《Journal of Data and Information Science》 CSCD 2024年第2期81-103,共23页
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t... Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution. 展开更多
关键词 multi-label classification Real-World datasets Hierarchical structure classification system Label correlation Machine learning
下载PDF
Inverse design of nonlinear phononic crystal configurations based on multi-label classification learning neural networks
2
作者 Kunqi Huang Yiran Lin +1 位作者 Yun Lai Xiaozhou Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第10期295-301,共7页
Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic feature... Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Traditional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials. 展开更多
关键词 multi-label classification learning nonlinear phononic crystals inverse design
下载PDF
Classification research of TCM pulse conditions based on multi-label voice analysis
3
作者 Haoran Shen Junjie Cao +5 位作者 Lin Zhang Jing Li Jianghong Liu Zhiyuan Chu Shifeng Wang Yanjiang Qiao 《Journal of Traditional Chinese Medical Sciences》 CAS 2024年第2期172-179,共8页
Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry poin... Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment. 展开更多
关键词 Pulse conditions TCM pulse diagnosis Voice analysis multi-label classification Machine learning
下载PDF
Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization
4
作者 Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi +3 位作者 Reza Salehi Diego Martín Zahra Halimi Sahba Baniasadi 《Computers, Materials & Continua》 SCIE EI 2024年第6期3469-3493,共25页
Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving ... Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process. 展开更多
关键词 Hyperspectral image classification reinforcement learning multi-objective binary grey wolf optimizer band selection
下载PDF
Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification
5
作者 Jieren Cheng Xiaolong Chen +3 位作者 Wenghang Xu Shuai Hua Zhu Tang Victor S.Sheng 《Computers, Materials & Continua》 SCIE EI 2023年第11期1779-1793,共15页
In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in sema... In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models. 展开更多
关键词 multi-label text classification feature extraction label distribution information sequence generation
下载PDF
Multi-label dimensionality reduction and classification with extreme learning machines 被引量:9
6
作者 Lin Feng Jing Wang +1 位作者 Shenglan Liu Yao Xiao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第3期502-513,共12页
In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the researc... In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis (MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis (LDA). In the classification process of multi-label data, the extreme learning machine (ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label linear discriminant analysis (MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification. 展开更多
关键词 multi-label dimensionality reduction kernel trick classification.
下载PDF
STATISTICAL CLASSIFICATION AND CHARACTERISTICS ANALYSIS OF BINARY TROPICAL CYCLONES OVER THE WESTERN NORTH PACIFIC OCEAN 被引量:3
7
作者 吴限 费建芳 +2 位作者 黄小刚 程小平 任建奇 《Journal of Tropical Meteorology》 SCIE 2011年第4期335-344,共10页
Using the 1949-2007 western North Pacific tropical cyclones (TCs) best-track data archived at the Shanghai Typhoon Institute of China Meteorological Administration for the western North Pacific from 1949 to 2007,both ... Using the 1949-2007 western North Pacific tropical cyclones (TCs) best-track data archived at the Shanghai Typhoon Institute of China Meteorological Administration for the western North Pacific from 1949 to 2007,both the characteristics of binary and multiple TCs and samples of interactions among TCs and multi-TCs are identified and statistically analyzed.According to the various features of individual TC tracks and interacting tracks,seven distinct types are proposed to describe the binary system of TCs and their interaction samples.The mean trajectories of the west and east component of binary TCs in each type are obtained using a new cluster analysis technique.These types are then analyzed in terms of landfall process,occurrence seasonality,coexistent lifetime,especially the large-scale patterns of atmospheric circulation.Finally,typical steering flows and conceptual models of the binary TCs at different phases are established based on six-hourly flow maps of the binary system and the averages are determined of the mean steering flow of ten representative binary TCs.Then,typical steering flows and conceptual models at the beginning,middle and final phase in each type are established to describe the large-scale circulation patterns of the binary system interaction types. 展开更多
关键词 binary tropical cyclones LANDFALL statistical classification steering flow
下载PDF
Study on Multi-Label Classification of Medical Dispute Documents 被引量:2
8
作者 Baili Zhang Shan Zhou +2 位作者 Le Yang Jianhua Lv Mingjun Zhong 《Computers, Materials & Continua》 SCIE EI 2020年第12期1975-1986,共12页
The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treat... The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods. 展开更多
关键词 Internet of Medical Things(IoMT) medical disputes medical dispute document(MDD) multi-label classification(MLC) key sentence extraction class imbalance
下载PDF
ML-ANet:A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving
9
作者 Guofa Li Zefeng Ji +3 位作者 Yunlong Chang Shen Li Xingda Qu Dongpu Cao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第5期107-117,共11页
To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden repre... To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space(RKHS)so that the mean-embeddings of specific features in different domains could be precisely matched.Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching,which can further reduce domain discrepancy.Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet.The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments.These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios. 展开更多
关键词 Autonomous vehicles Deep learning Image classification multi-label learning Transfer learning
下载PDF
Multi-label learning of face demographic classification for correlation analysis
10
作者 方昱春 程功 罗婕 《Journal of Shanghai University(English Edition)》 CAS 2011年第5期352-356,共5页
In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most po... In this paper, we utilize the framework of multi-label learning for face demographic classification. We also attempt t;o explore the suitable classifiers and features for face demographic classification. Three most popular demographic information, gender, ethnicity and age are considered in experiments. Based on the results from demographic classification, we utilize statistic analysis to explore the correlation among various face demographic information. Through the analysis, we draw several conclusions on the correlation and interaction among these high-level face semantic, and the obtained results can be helpful in automatic face semantic annotation and other face analysis tasks. 展开更多
关键词 denlographic classification multi-label learning face analysis
下载PDF
Pedestrian attribute classification with multi-scale and multi-label convolutional neural networks
11
作者 朱建清 Zeng Huanqiang +2 位作者 Zhang Yuzhao Zheng Lixin Cai Canhui 《High Technology Letters》 EI CAS 2018年第1期53-61,共9页
Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label c... Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin. 展开更多
关键词 PEDESTRIAN ATTRIBUTE classification MULTI-SCALE features multi-label classification convolutional NEURAL network (CNN)
下载PDF
Using Hybrid and Diversity-Based Adaptive Ensemble Method for Binary Classification
12
作者 Xing Fan Chung-Horng Lung Samuel A. Ajila 《International Journal of Intelligence Science》 2018年第3期43-74,共32页
This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adapt... This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers. 展开更多
关键词 Bigdata ANALYTICS MACHINE Learning ADAPTIVE ENSEMBLE Methods binary classification
下载PDF
An Efficient Smooth Quantile Boost Algorithm for Binary Classification
13
作者 Zhefeng Wang Wanzhou Ye 《Advances in Pure Mathematics》 2016年第9期615-624,共10页
In this paper, we propose a Smooth Quantile Boost Classification (SQBC) algorithm for binary classification problem. The SQBC algorithm directly uses a smooth function to approximate the “check function” of the quan... In this paper, we propose a Smooth Quantile Boost Classification (SQBC) algorithm for binary classification problem. The SQBC algorithm directly uses a smooth function to approximate the “check function” of the quantile regression. Compared to other boost-based classification algorithms, the proposed algorithm is more accurate, flexible and robust to noisy predictors. Furthermore, the SQBC algorithm also can work well in high dimensional space. Extensive numerical experiments show that our proposed method has better performance on randomly simulations and real data. 展开更多
关键词 BOOSTING Quantile Regression Smooth Check Function binary classification
下载PDF
A Multi-Label Classification Method for Vehicle Video
14
作者 Yanqiu Cao Chao Tan Genlin Ji 《Journal on Big Data》 2020年第1期19-31,共13页
In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity sc... In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity scene,one of the major challenges is mining information comprehensively from massive features in vehicle video.This paper proposes a multi-label classification method MCM-VV(Multi-label Classification Method for Vehicle Video)for vehicle video to judge the label of road condition for unmanned system.Method MCM-VV includes a process of feature extraction and a process of multi-label classification.During feature extraction,grayscale,lane line and the edge of main object are extracted after video preprocessing.During the multi-label classification,the algorithm DR-ML-KNN(Multi-label K-nearest Neighbor Classification Algorithm based on Dimensionality Reduction)learns the training set to obtain multi-label classifier,then predicts the label of road condition according to maximum a posteriori principle,finally outputs labels and adds the new instance to training set for the optimization of classifier.Experimental results on five vehicle video datasets show that the method MCM-VV is effective and efficient.The DR-ML-KNN algorithm reduces the runtime by 50%.It also reduces the time complexity and improves the accuracy. 展开更多
关键词 Vehicle video multi-label classification dimensionality reduction
下载PDF
Residents'satisfaction of Beijing new regulations for domestic waste classification based on binary logistic regression:A case study of Daxing District
15
作者 GU Yue-qi HOU Xiao-yu +2 位作者 LI Si-tong TIAN Li ZHOU Yan-fang 《Ecological Economy》 2022年第3期190-204,共15页
On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted... On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process. 展开更多
关键词 domestic waste classification policy residents’satisfaction binary logistic regression influencing factors
下载PDF
Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs
16
作者 Christos Barbagiannis Alexios Polydorou +2 位作者 Michail Zervakis Andreas Polydorou Eleftheria Sergaki 《Journal of Biomedical Science and Engineering》 2021年第12期402-414,共13页
The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landm... The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data. 展开更多
关键词 Capsule Endoscopy (CE) Small Bowel Bleeding (SBB) Angioectasia Haemorrhage Gatrointestinal (GI) Small Bowel Capsule Endoscopy (SBCE) Convolutional Neural Network (CNN) Computer Aided Diagnosis (CAD) Image Level Annotation Pixel Level Annotation binary classification
下载PDF
Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm
17
作者 ZHANG Yongwei 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第1期51-58,共8页
Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label c... Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification.Although this technique is widely used in multi-label classification problems,batch learning deals with most issues,which consumes a lot of time and space resources.Unlike traditional batch learning methods,online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets.However,existing online learning research has done little to consider correlations between labels.On the basis of existing research,this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples.We evaluate the performance of the proposed algorithm on several public datasets.Experiments show the effectiveness of our algorithm. 展开更多
关键词 label correlations passive aggressive multi-label classification online learning
原文传递
A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation
18
作者 Feng Sun Ming-Kun Xie Sheng-Jun Huang 《Machine Intelligence Research》 EI CSCD 2024年第4期801-814,共14页
In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML metho... In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real tasks.Furthermore,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on mini-batches.In this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination ability.On the one hand,we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes.On the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantlyoutperforms the SOTA methods. 展开更多
关键词 Partial multi-label image classification curriculum-based disambiguation consistency regularization label difficulty candidatelabel set.
原文传递
Product Image Classification Based on Fusion Features
19
作者 杨晓慧 刘静静 杨利军 《Chinese Quarterly Journal of Mathematics》 2015年第3期429-441,共13页
Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images cl... Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3. 展开更多
关键词 product image classification FAN refined local binary pattern(FRLBP) PYRAMID HISTOGRAM of orientated gradients(PHOG) FUSION FEATURES
下载PDF
Hybridization of CNN with LBP for Classification of Melanoma Images
20
作者 Saeed Iqbal Adnan N.Qureshi Ghulam Mustafa 《Computers, Materials & Continua》 SCIE EI 2022年第6期4915-4939,共25页
Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesi... Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality.To this end,we have designed,implemented and analyzed a hybrid approach entailing convolutional neural networks(CNN)and local binary patterns(LBP).The experiments have been performed on publicly accessible datasets ISIC 2017,2018 and 2019(HAM10000)with data augmentation for in-distribution generalization.As a novel contribution,the CNN architecture is enhanced with an intelligible layer,LBP,that extracts the pertinent visual patterns.Classification of Basal Cell Carcinoma,Actinic Keratosis,Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing,respectively.Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%,sensitivity of 95.63%and specificity of 97.90%.Hence,the proposed approach can be used in research and clinical settings to provide second opinions,closely approximating experts’intuition. 展开更多
关键词 Skin cancer convolutional neural network feature extraction local binary pattern classification
下载PDF
上一页 1 2 25 下一页 到第
使用帮助 返回顶部