BITS Faculty Publications
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Item Universum least squares twin parametric-margin support vector machine(IEEE, 2020-07) Richhariya, BharatUniversum based algorithms involve universum samples in the classification problem to improve the generalization performance. In order to provide prior information about data, we utilized universum data to propose a novel classification algorithm. In this paper, a novel parametric model for universum based twin support vector machine is presented for classification problems. The proposed model is termed as universum least squares twin parametric-margin support vector machine (ULSTPMSVM). The solution of ULSTPMSVM involves a system of linear equations. This makes the ULSTPMSVM efficient w.r.t. training time. In order to verify the performance of the proposed model, various experiments are carried out on real world benchmark datasets. Statistical tests are performed to verify the significance of the proposed method. The proposed ULSTPMSVM performed better than existing algorithms in terms of classification accuracy and training time for most of the datasets. Moreover, an application of proposed ULSTPMSVM is presented for classification of Alzheimer's disease data.Item Facial expression recognition using iterative universum twin support vector machine(Elsevier, 2019-03) Richhariya, BharatFacial expressions are one of the most important characteristics of human behaviour. They are very useful in applications on human computer interaction. To classify facial emotions, different feature extraction methods are used with machine learning techniques. In supervised learning, information about the distribution of data is given by data points not belonging to any of the classes. These data points are known as universum data. In this work, we use universum data to perform multiclass classification of facial emotions from human facial images. Moreover, the existing universum based models suffer from the drawback of high training cost, so we propose an iterative universum twin support vector machine (IUTWSVM) using Newton method. Our IUTWSVM gives good generalization performance with less computation cost. To solve the optimization problem of proposed IUTWSVM, no optimization toolbox is required. Further, improper selection of universum points always leads to degraded performance of the model. For generating better universum, a novel scheme is proposed in this work based on information entropy of data. To check the effectiveness of proposed IUTWSVM, several numerical experiments are performed on benchmark real world datasets. For multiclass classification of facial emotions, the performance of IUTWSVM is compared with existing algorithms using different feature extraction techniques. Our proposed algorithm shows better generalization performance with less training cost in both binary as well as multiclass classification problems.Item A reduced universum twin support vector machine for class imbalance learning(Elsevier, 2020-06) Richhariya, BharatIn most of the real world datasets, there is an imbalance in the number of samples belonging to different classes. Various pattern classification problems such as fault or disease detection involve class imbalanced data. The support vector machine (SVM) classifier becomes biased towards the majority class due to class imbalance. Moreover, in the existing SVM based techniques for class imbalance, there is no information about the distribution of data. Motivated by the idea of prior information about data distribution, a reduced universum twin support vector machine for class imbalance learning (RUTSVM-CIL) is proposed in this paper. For the first time, universum learning is incorporated with SVM to solve the problem of class imbalance. Oversampling and undersampling of data is performed to remove the imbalance in the classes. The universum data points are used to give prior information about the data. To reduce the computation time of our universum based algorithm, we use a small sized rectangular kernel matrix. The reduced kernel matrix needs less storage space, and thus applicable for large scale imbalanced datasets. Comprehensive experimentation is performed on various synthetic, real world and large scale imbalanced datasets. In comparison to the existing approaches for class imbalance, the proposed RUTSVM-CIL gives better generalization performance for most of the benchmark datasets. Also, the computation cost of RUTSVM-CIL is very less, making it suitable for real world applications.Item Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE)(Elsevier, 2020-05) Richhariya, BharatAlzheimer's disease is one of the most common causes of death in today's world. Magnetic resonance imaging (MRI) provides an efficient and non-invasive approach for diagnosis of Alzheimer's disease. Efficient feature extraction techniques are needed for accurate classification of MRI images. Motivated by the work on support vector machine based recursive feature elimination (SVM-RFE) [16], we propose a novel feature selection technique to incorporate prior information about data distribution in the recursive feature elimination process. Our method is termed as universum support vector machine based recursive feature elimination (USVM-RFE). The proposed method provides global information about data in the RFE process as compared to the local approach of feature selection in SVM-RFE. We also present the application of feature selection and classification algorithms on both voxel based as well as volume based morphometry analysis of structural MRI images (ADNI database). Feature selection is performed using MRI data of brain tissues such as gray matter, white matter, and cerebrospinal fluid. USVM-RFE provides improvement over SVM-RFE in classification of control normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects. Moreover, better accuracy is obtained by USVM-RFE with lesser number of features in comparison to SVM-RFE. This leads to identification of prominent brain regions for feature selection and classification of MRI images. The highest accuracies obtained by our method for classification of CN vs AD, CN vs MCI, and MCI vs AD are 100%, 90%, and 73.68%, respectively.Item EEG signal classification using universum support vector machine(Elsevier, 2018-09) Richhariya, BharatSupport vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders. SVM shows good generalization performance for high dimensional data due to its convex optimization problem. The incorporation of prior knowledge about the data leads to a better optimized classifier. Different types of EEG signals provide information about the distribution of EEG data. To include prior information in the classification of EEG signals, we propose a novel machine learning approach based on universum support vector machine (USVM) for classification. In our approach, the universum data points are generated by selecting universum from the EEG dataset itself which are the interictal EEG signals. This removes the effect of outliers on the generation of universum data. Further, to reduce the computation time, we use our approach of universum selection with universum twin support vector machine (UTSVM) which has less computational cost in comparison to traditional SVM. For checking the validity of our proposed methods, we use various feature extraction techniques for different datasets consisting of healthy and seizure signals. Several numerical experiments are performed on the generated datasets and the results of our proposed approach are compared with other baseline methods. Our proposed USVM and proposed UTSVM show better generalization performance compared to SVM, USVM, Twin SVM (TWSVM) and UTSVM. The proposed UTSVM has achieved highest classification accuracy of 99% for the healthy and seizure EEG signals.