Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases: (i) to select feature sets that are likely to be the support vectors by applying ensemble based feature selection methods;and (ii) to construct an SVM ensemble using the selected features. The proposed approach was evaluated by experiments on Cardiotocography dataset. Four feature selection techniques were used: (i) Correlation-based, (ii) Consistency-based, (iii) ReliefF and (iv) Information Gain. Experimental results showed that using the ensemble of Information Gain feature selection and Correlation-based feature selection with SVM ensembles achieved higher classification accuracy than both single SVM classifier and ensemble feature selection with SVM classifier.

Extracting Feature Bands for Damaged Rice Leaves by Planthoppers Using Multi-spectral Imaging Technology

[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops.

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

The advent of additive technologies has provided a significant breakthrough in the production of medical implants.It has reduced costs,increased productivity and accuracy of the implant manufacturing process.However,there are problems associated with assessing defects in the microstructure,mechanical and technological properties of alloys,both during their production by powder metallurgy and in the process of 3D printing.Thus traditional research methods of alloys properties demand considerable human,material,and time resources.At the same time,artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure,phase composition,and properties of titanium powder’s alloys.It provides new possibilities for the efficient production of biocompatible implants for various functional purposes.However,the accuracy of the methods and models used should be as high as possible.In this paper we designed a hybrid PNN-SVM(Probabilistic Neural Network-Support Vector Machine)high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants.We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology.Subsequent classification based on the expanded dataset is performed using SVM.We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr.We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods.

Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

Support vector machines （SVMs） are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM （GDB-SVM）. It takes the distance between a point and classified hyperplane as classification rule,and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C ＋＋ 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all （OAA）. Three predicted results of five of GDB-SVM are better than those of the method of one against one （OAO）. Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.

Land cover mapping of deciduous forest regions using ETM+ data: a case study of Azerbaijan Province, Iran

Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.

Alzheimer’s Disease: The Relative Importance Diagnostic

As the population ages, Alzheimer’s disease is rapidly increasing, and the diagnosis of the disease is still poorly understood. In comparison to cancer, 90% of patients become aware of their diagnosis, but only 45% of the people with Alzheimer’s are aware. Thus, the need for biomarkers for reliable diagnosis is tremendous to help in finding treatment for this serious disease. Hence, the main aim of this paper is to utilize information from baseline measurements to develop a statistical prediction model using multiple logistic regression to distinguish Alzheimer’s disease patients from cognitively normal individuals. Our optimal predictive model includes six risk factors and two interaction terms and has been evaluated using classification accuracy, sensitivity, specificity values and area under the curve.

Sparse Proximal Support Vector Machine with a Specialized Interior-Point Method

Support vector machine(SVM)is a widely used method for classification.Proximal support vector machine(PSVM)is an extension of SVM and a promisingmethod to lead to a fast and simple algorithm for generating a classifier.Motivated by the fast computational efforts of PSVM and the properties of sparse solution yielded by l1-norm,in this paper,we first propose a PSVM with a cardinality constraint which is eventually relaxed byl1-norm and leads to a trade-offl1−l2 regularized sparse PSVM.Next we convert thisl1−l2 regularized sparse PSVM into an equivalent form of1 regularized least squares(LS)and solve it by a specialized interior-point method proposed by Kim et al.(J SelTop Signal Process 12:1932–4553,2007).Finally,l1−l2 regularized sparse PSVM is illustrated by means of a real-world dataset taken from the University of California,Irvine Machine Learning Repository(UCI Repository).Moreover,we compare the numerical results with the existing models such as generalized eigenvalue proximal SVM(GEPSVM),PSVM,and SVM-Light.The numerical results showthat thel1−l2 regularized sparsePSVMachieves not only better accuracy rate of classification than those of GEPSVM,PSVM,and SVM-Light,but also a sparser classifier compared with the1-PSVM.

Land cover mapping applications with MODIS:a literature review

Land use/land cover monitoring and mapping is crucial to efficient management of the land and its resources.Since the late 1980s increased attention has been paid to the use of coarse resolution optical data.The Moderate Resolution Imaging Spectroradiometer(MODIS)has features,which make it particularly suitable to earth characterization purposes.MODIS has 10 products dedicated mainly to land cover characterization and provides three kinds of data:angular,spectral and temporal.MODIS data also includes information about the data quality through the‘Quality Assessment’product.In this paper,we review how MODIS data are used to map land cover including the preferred MODIS products,the preprocessing and classification approaches,the accuracy assessment,and the results obtained.

Comparing EMG Pattern Recognition with and Without Hand and Wrist Movements

Electromyography(EMG)pattern recognition has been widely employed for prosthesis control.Several studies demonstrated that amputees had poorer performances of EMG pattern recognition when compared to able-bodied individuals.Several factors,such as the muscle weakness and atrophy of residual limbs,the length of residual limbs,and the decrease of the affected side's motor cortex,had been studied to improve the performance of amputees.However,there was no study on the factor that the absence of joint movements for amputees.This study aimed to investigate whether the hand and wrist joint movements had effects on the EMG pattern recognition.Ten able-bodied subjects were tested for 11 hand and wrist gestures with two different gesture modalities:hand and wrist joints unconstrained(HAWJU)and constrained(HAWJC).Time-domain(TD)features and Linear Discriminant Analysis(LDA)were employed to compare the classification performance of the two modalities.Compared to HAWJU,HAWJC significantly reduced the average Classification Accuracy(CA)across all subjects from 95.53 to 85.52%.The experimental results demonstrated that the hand and wrist joint movements had significant effects on EMG pattern recognition.The outcomes provided a new perspective to study the factors affecting EMG pattern recognition.