Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-high-dimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

基于手势多特征融合及优化Multiclass-SVC的手势识别

深度相机的发展使得获取手势骨骼信息更加方便,为了从多维手势骨骼节点大数据中获取有用信息并在室内复杂环境和近距离条件下实现对常见双手静态交互动作的识别,提出一种基于多特征融合及生物启发式遗传算法优化多分类支持向量分类器(multiclass-SVC)的静态手势识别方法。利用手势骨骼数据设计了新的手势特征且通过特征组合策略建立更全面的手势特征序列,削弱了冗余特征产生的影响,提高了数据处理能力;采用生物启发式遗传算法优化multiclass-SVC的核函数与惩罚参数,得到最优核函数和惩罚参数,能够克服因随机选择核函数和惩罚参数导致手势识别准确度低的缺点;运用P、R、F1、A度量指标对手势识别模型进行综合评估,且通过与KNN、MLP、MLR、XGboost等模型的对比实验,验证了所提手势识别模型能有效提高手势识别准确度;通过迭代增加手势样本数据进行模型训练的方法分析了样本容量对手势识别准确度的影响,提供了一种提高手势识别准确度的有效方法。实验结果表明,手势识别准确率达到98.4%,识别算法的查准率、查全率和F1性能评测指标均值不低于0.98。

Salting-Out Assisted Liquid-Liquid Extraction Combined with HPLC for Quantitative Extraction of Trace Multiclass Pesticide Residues from Environmental Waters

In this study, salting-out assisted liquid-liquid extraction combined with high performance liquid chromatography diode array detector (SALLE-HPLC-DAD) method was developed and validated for simultaneous analysis of carbaryl, atrazine, propazine, chlorothalonil, dimethametryn and terbutryn in environmental water samples. Parameters affecting the extraction efficiency such as type and volume of extraction solvent, sample volume, salt type and amount, centrifugation speed and time, and sample pH were optimized. Under the optimum extraction conditions the method was linear over the range of 10 - 100 μg/L (carbaryl), 8 - 100 μg/L (atarzine), 7 - 100 μg/L (propazine) and 9 - 100 μg/L (chlorothalonil, terbutryn and dimethametryn) with correlation coefficients (R2) between 0.99 and 0.999. Limits of detection and quantification ranged from 2.0 to 2.8 μg/L and 6.7 to 9.5 μg/L, respectively. The extraction recoveries obtained for ground, lake and river waters were in a range of 75.5% to 106.6%, with the intra-day and inter-day relative standard deviation lower than 3.4% for all the target analytes. All of the target analytes were not detected in these samples. Therefore, the proposed SALLE-HPLC-DAD method is simple, rapid, cheap and environmentally friendly for the determination of the aforementioned herbicides, insecticide and fungicide residues in environmental water samples.

Solving large-scale multiclass learning problems via an efficient support vector classifier

Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.

High Density Solvent Based Dispersive Liquid-Liquid Microextraction Technique for Simultaneous and Selective Extraction of Multiclass Pesticide Residues in Water and Sugarcane Juice Samples

In this study, a miniaturized analytical technique based on high density solvent based dispersive liquid-liquid microextraction (HD-DLLME) was developed for extraction of trace residues of multiclass pesticides including three striazine herbicides, two organophosphate insecticides and two organochlorine fungicides from environmental water and sugarcane juice samples. The analytical method was validated and found to offer good linearity: R2 ≥ 0.991;repeatability varied from 0.73% - 5.28%;reproducibility varied from 1.14% - 8.74% and limit of detection ranged from 0.005 to 0.02 μg/L. Moreover, accuracy of the optimized method was evaluated and the recovery was varied from 80.39% - 114.05%. Analytical applications of this method to environmental waters and sugarcane juice samples indicate the presence of trace residues of ametryn in the lake water and sugarcane juice samples. Atrazine and ametryn were also detected in irrigation water.

Model-Free Feature Screening Based on Gini Impurity for Ultrahigh-Dimensional Multiclass Classification

It is quite common that both categorical and continuous covariates appear in the data. But, most feature screening methods for ultrahigh-dimensional classification assume the covariates are continuous. And applicable feature screening method is very limited;to handle this non-trivial situation, we propose a model-free feature screening for ultrahigh-dimensional multi-classification with both categorical and continuous covariates. The proposed feature screening method will be based on Gini impurity to evaluate the prediction power of covariates. Under certain regularity conditions, it is proved that the proposed screening procedure possesses the sure screening property and ranking consistency properties. We demonstrate the finite sample performance of the proposed procedure by simulation studies and illustrate using real data analysis.

Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.

Robust Multiclass Classification for Learning from Imbalanced Biomedical Data

tmbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has attracted a lot of research interests in the past decade. Unfortunately, most research efforts only concentrate on 2-class problems. In this paper, we study a new method of formulating a multiclass Support Vector Machine （SVM） problem for imbalanced biomedical data to improve the classification performance. The proposed method applies cost-sensitive approach and ramp loss function to the Crammer and Singer multiclass SVM formulation. Experimental results on multiple biomedical datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.

基于语义分割的织物疵点检测算法研究

针对织物疵点语义分割任务中数据分类不均衡导致疵点检测准确率不高的问题,文章在Resnet、U-net网络结构基础上设计了CS model网络,添加了适用于小疵点及条带状疵点特征检测的MSCA注意力机制。织物图像中,破洞、污渍等织物疵点像素,占比较少,相比于全图像素为小类别疵点,导致分割结果不准确。针对小类别疵点分割准确率不高的问题,将多类别Focal Loss损失函数引入于其中,该损失函数通过提高小类别疵点的权值,使分割结果更为准确。调整Focal Loss参数对比实验结果,采用mIoU、Acc和Loss数值作为实验评价指标,分别与U-Net、ResNet50、DeepLabV3和VGG16网络的语义分割模型进行对比实验,结果表明:提出的CS model网络可将小类别疵点分割精度有效提高几个百分点。

DGA-Based Botnet Detection Toward Imbalanced Multiclass Learning

Botnets based on the Domain Generation Algorithm(DGA) mechanism pose great challenges to the main current detection methods because of their strong concealment and robustness. However, the complexity of the DGA family and the imbalance of samples continue to impede research on DGA detection. In the existing work, the sample size of each DGA family is regarded as the most important determinant of the resampling proportion;thus,differences in the characteristics of various samples are ignored, and the optimal resampling effect is not achieved.In this paper, a Long Short-Term Memory-based Property and Quantity Dependent Optimization(LSTM.PQDO)method is proposed. This method takes advantage of LSTM to automatically mine the comprehensive features of DGA domain names. It iterates the resampling proportion with the optimal solution based on a comprehensive consideration of the original number and characteristics of the samples to heuristically search for a better solution around the initial solution in the right direction;thus, dynamic optimization of the resampling proportion is realized.The experimental results show that the LSTM.PQDO method can achieve better performance compared with existing models to overcome the difficulties of unbalanced datasets;moreover, it can function as a reference for sample resampling tasks in similar scenarios.

蒙古族服装线上定制服务营销策略研究

文章采用K-means算法对消费市场进行细分并锁定目标群体,通过无序多分类Logistic回归分析模型筛选影响消费者定制的因素,提出以产品和渠道为中心,通过打造品牌IP及提高线上定制体验等方式,实现企业服务与消费需求的有效融合,为制定蒙古族服装线上定制服务营销策略提供参考。

基于解纠缠表示学习的人脸反欺骗算法

针对现有人脸反欺骗模型面对不同应用场景识别精度低、泛化性能不佳的问题,引入解纠缠表示学习,提出一种基于解纠缠表示学习的人脸反欺骗方法。该方法采用U-Net架构和ResNet-18作为编/解码器。首阶段训练中,通过输入真实样本使得编码器仅学习到真实样本相关信息。第二阶段,构建对抗性学习网络,输入不具标签的样本,将预训练的编码器输出和新编码器输出进行特征融合,由解码器重建图像,在鉴别器中与原始图像进行对抗训练,以实现特征的解耦。模型与一些经典人脸反欺骗方法相比,有着更好的检测性能,在OULU-NPU数据集的数个实验中,最低的检测错误率仅为0.8%,表现优于STDN等经典检测方法。该人脸反欺骗方法通过分阶段训练的方式,使得模型在对抗性训练中获得了相比端到端模型更具判别性的特征表示,在欺骗特征图输出阶段采用多分类策略,减小了不同的图像噪声对分类结果的影响,在公开数据集上的实验验证了算法的有效性。

基于数据挖掘的路面交通信息预警模型分析

为减少道路交通事故的发生,基于近年交通事故数据分析道路交通事故规律及成因,以照明条件、能见度和天气等5个因素为自变量,以无伤害、轻伤、重伤3种交通事故严重程度为因变量,采用多元Logistic回归模型和有序多分类Logistic回归模型,分析影响交通事故严重程度的重要因素。对交通事故数据进行验证,结果表明:多元Logistic回归模型和有序多分类Logistic回归模型对交通事故严重程度的正确预测率分别为73.1%、75.0%。基于数据挖掘的道路交通事故成因分析可为交通管理部门治理交通环境、降低交通事故提供依据。

One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems,the skewed distribution of multiclass data poses a major challenge to machine learning algorithms.To tackle such issues,we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance(OAHD).Two crucial elements are included in OAHD.First,the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD,thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem.Second,for the multiclass imbalance problem,the distribution and the number of distinct classes are taken into account,and a modified Gini index is designed.Moreover,we give theoretical proofs for the properties of OAHD,including skew insensitivity and the ability to seek a purer node in the decision tree.Finally,we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning(KEEL)repository and the University of California,Irvine(UCI)repository.Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision,F-measure,and multiclass area under the receiver operating characteristic curve(MAUC).Moreover,through statistical analysis,the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.

Classifying multiclass relationships between ASes using graph convolutional network

Precisely understanding the business relationships between autonomous systems(ASes)is essential for studying the Internet structure.To date,many inference algorithms,which mainly focus on peer-to-peer(P2P)and provider-to-customer(P2C)binary classification,have been proposed to classify the AS relationships and have achieved excellent results.However,business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years.Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships.In this study,we focus on multiclassification of AS relationship for the first time.We first summarize the differences between AS relationships under the structural and attribute features,and the reasons why multiclass relationships are difficult to be inferred.We then introduce new features and propose a graph convolutional network(GCN)framework,AS-GCN,to solve this multiclassification problem under complex scenes.The proposed framework considers the global network structure and local link features concurrently.Experiments on real Internet topological data validate the effectiveness of our method,that is,AS-GCN.The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task,with an overall metrics above 95%.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

Pancreatic Cancer Data Classification with Quantum Machine Learning

Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems,which are pervasive in machine learning.Quantum Support Vector Machine(QSVM)is a quantum machine learning algorithm inspired by classical Support Vector Machine(SVM)that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces.We provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks.Through a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality.As a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker data.The Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework.The classical approach as well as the quantum-based techniques reported similar accuracy.This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset.Remarkably,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency.This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.

基于改进的3D U-Net骨盆CT影像多类分割

骨盆CT影像精确分割是骨盆骨疾病的临床诊断和手术规划中非常重要的环节。针对目前2D骨盆分割方法对三维医学影像进行切片处理时损失空间信息的问题,提出了改进3D U-Net网络实现对骨盆CT影像3D自动分割。实验数据为公开数据集CTPelvic1K共1184名患者骨盆CT影像,其中包含骶骨、左髋骨、右髋骨和腰椎四个部位标签。以3D U-Net骨干网络为基础,结合自注意力机制提出3D多类分割模型3D Trans U-Net,并使用迁移学习训练3D U-Net、V-Net、Attention U-Net作为对照实验。实验结果表明:3D Trans U-Net在测试集上整个骨盆区域、骶骨、左髋骨、右髋骨、腰椎Dice系数分别达到97.99%,96.70%,97.96%,97.95%,96.89%;Dice系数、豪斯多夫距离等评价指标均优于现有经典网络3D U-Net、V-Net、Attention U-Net。因此,改进的3D Trans U-Net对骨盆不同部位具有较好的分割效果,为精准医治骨盆骨疾病提供了一条有效的技术途径。

基于双层规划模型的高速公路差异化收费定价研究

为了提高高速公路利用率,减轻平行国省道的交通负担,综合考虑高速公路经营者和使用者双方的利益,建立了以实现高速公路运营收益增加为上层目标,以实现路网多用户随机均衡分配为下层目标的差异化收费定价模型,设计了遗传算法和模拟退火算法与迭代加权法组合的模型求解算法。在分析山东省龙青高速及其平行国省道交通流特征的基础上,运用设计的模型有针对性地制定分出入口路段、分时段、分车型的高速公差异化收费方案。研究结果表明最优的差异化收费方案能使龙青高速北向南方向的运营收益增加786.19万元/年,道路使用者的出行成本减少716.51万元/年,验证了差异化收费定价模型的实用性,进一步完善了高速公路多模式差异化收费方案。