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Epilepsy is one of the most common neurological diseases, affecting approximately 50 million people. This illness can be diagnosed by electroencephalogram (EEG), whose analysis depends on human interpretation, which may lead to divergent results and be imprecise and error-prone. Moreover, one estimates more than 80% of the epilepsy examinations return no anomalies at all, wasting the effort of analysis. In this context, machine learning (ML) methods are used to aid in epilepsy detection. An essential task for classifier building is feature extraction, for which there are many measures that can be computed. However, part of these features can be irrelevant or hinder the performance of ML methods. This paper proposes an automatic way to classify EEG segments by using a reduced set of features. The proposed method combines multispectral analysis, feature selection, and classifier building approaches. As our main contribution, a methodology to minimize the number of features for classifier building is proposed. A total of 285 measures are extracted. Afterward, two attribute selection approaches are used: Pearson's correlation coefficient filter and wrapper based on the genetic algorithm. Then, five well-known ML methods (k-nearest-neighbor, support vector machine, naive Bayes, artificial neural network, and random forest) were used to build 281 different classifiers. As a result, the proposed classifiers reached an accuracy between 87.2 and 90.99% and considerably reduced the number of features from 285 to 30, keeping competitive scores. Additionally, statistical hypothesis tests prove that our proposed approach is as efficient as using the complete feature dataset for classifier building. [ABSTRACT FROM AUTHOR]

The quest to improve performance accuracy and prediction speed in machine learning algorithms cannot be overemphasized, as the need for machines to outperform humans continue to grow. Accordingly, several studies have proposed methods to improve prediction performance and speed particularly for spatio-temporal analysis. This study proposes a novel classifier that leverages ensemble techniques to improve prediction performance and speed. The proposed classifier, Ada-AdaSVM uses an AdaBoost feature selection algorithm to select small features of input datasets for a joint support vector machine (SVM)–AdaBoost classifier. The proposition is evaluated against a selection of existing classifiers (SVM, AdaSVM and AdaBoost) using the Jaffe, Yale, Taiwanese facial expression database (TFEID) and CK + 48 datasets with Haar features as the preferred method for feature extraction. The findings indicated that Ada-AdaSVM outperforms SVM, AdaSVM and AdaBoost classifiers in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved computer-aided diagnosis model to identify mammographic images as normal or abnormal, and further, benign or malignant. The proposed scheme employs all the steps associated with other classification schemes; however, the contribution of the suggested scheme is fourfold. Initially, a fusion-based feature extraction method is employed to obtain the features using a combination of 2-D block discrete wavelet transform (2D-BDWT) and gray-level co-occurrence matrix (GLCM). Next, principal component analysis (PCA) is utilized to reduce the large dimension of the feature vector. Furthermore, to select the most optimal features from the reduced set of features, forest optimization algorithm (FOA) is used. The FOA-based feature selection algorithm is utilized as a wrapper-based technique which includes both feature selection and classification. In the proposed framework, several classifiers, namely support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (C4.5), are applied. The proposed method is compared with the benchmark schemes on two standard datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). Simulation results and analysis confirm that the proposed scheme brings potential improvements with respect to classification accuracy, sensitivity, specificity, area under curve, F-score, and Matthews correlation coefficient. The classification accuracy is measured with respect to normal versus abnormal and further, benign versus malignant. The proposed scheme with different combinations of classifiers, namely 2D-BDWT + GLCM + PCA + FOA + SVM, 2D-BDWT + GLCM + PCA + FOA + k-NN, and 2D-BDWT + GLCM + PCA + FOA + C4.5, achieves a maximum classification accuracy of 100% for both the MIAS and DDSM datasets. [ABSTRACT FROM AUTHOR]

Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns. During the last few years, great efforts have been made to develop a number of scene classification methods for remote sensing images. However, the existing remote sensing image classification methods do not perform satisfactorily in dealing with multi-class classification problems and rely heavily on the quality of data sets. These disadvantages seriously restrict the application of remote sensing image, including industrial research, analysis and calculation of land use and land coverage. To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method. Specifically, after constructing bag of features by using speeded up robust features extraction algorithm, direct sparsity optimization-based feature selection method is applied for selecting discriminative features, which is used for constructing support vector machine classifier model. The proposed algorithm has been evaluated and compared with other advanced feature selection methods on four public remote sensing image data sets. The experimental results demonstrate the effectiveness of our proposed image classification algorithm, which has been successfully applied to remote sensing image classification tasks. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the classification of polarimetric SAR images has become an important part of the research of target recognition and image interpretation. However, for the research method is relatively simple and the accuracy is low, this paper carries out the work from the two aspects of feature extraction and feature classification of the ground object, and analyzes and studies the application and value of the polarimetric SAR system. The basic algorithm of polarization SAR image classification is proposed. A polarimetric SAR image feature classification method based on polarization target decomposition and support vector machine is proposed. Four kinds of scattering features and Freeman decomposition are obtained by Cloude decomposition. The simulation results show that the accuracy of using combined features is about 6.5% higher than that of single features. A polarization classification model based on polarization target decomposition and limit learning method is proposed. The simulation experiment shows ELM learning. The algorithm is indeed much faster than SVM learning. In this paper, a polarimetric SAR image classification method based on improved scattering mechanism coefficients is proposed, and the effectiveness of the polarimetric SAR image classification method based on improved scattering mechanism coefficients is verified. Experimental results show that after feature selection, the method of combining Freeman decomposition and Wishart classifier can get better classification results. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. SVMs have been used in a variety of applications. However, they are considered 'black box models,' where no interpretation about the input-output mapping is provided. Some methods to reduce this limitation have already been proposed, but they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. In order to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. The proposed model is suited for classification in multi-class problems and includes a wrapper feature selection algorithm. It is evaluated in four benchmark databases, and results obtained demonstrate its capacity to generate a reduced set of interpretable fuzzy rules that explains both the classification database and the influence of each input variable on the determination of the final class. [ABSTRACT FROM AUTHOR]