Kernel Support Vector Machine Classifier Research Articles

A novel tree kernel support vector machine classifier for modeling the relationship between bioactivity and molecular descriptors

Support vector machine (SVM) has been gaining popularity in the field of chemistry. However, it also suffered from the problems of feature subset selection in most of applications. In the present study, we attempt to construct an informative novel tree kernel to address these problems. The constructed tree kernel can effectively discover the similarities of samples and handle nonlinear classification problems. Simultaneously, informative features can be evaluated by variable importance ranking in the process of building kernel by a large number of decision trees. Thus, under the framework of kernel methods, a novel tree kernel support vector machine (TKSVM) has been proposed to model the structure–activity relationship between bioactivities and molecular structures. Three datasets related to different categorical bioactivities of compounds are used to test the performance of TKSVM. The results show that the present method is a promising one compared to the SVM models with other commonly used kernels.

On the Design and Analysis of the Privacy-Preserving SVM Classifier

The support vector machine (SVM) is a widely used tool in classification problems. The SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training data set, releasing the SVM classifier for public use or shipping the SVM classifier to clients will disclose the private content of support vectors. This violates the privacy-preserving requirements for some legal or commercial reasons. The problem is that the classifier learned by the SVM inherently violates the privacy. This privacy violation problem will restrict the applicability of the SVM. To the best of our knowledge, there has not been work extending the notion of privacy preservation to tackle this inherent privacy violation problem of the SVM classifier. In this paper, we exploit this privacy violation problem, and propose an approach to postprocess the SVM classifier to transform it to a privacy-preserving classifier which does not disclose the private content of support vectors. The postprocessed SVM classifier without exposing the private content of training data is called Privacy-Preserving SVM Classifier (abbreviated as PPSVC). The PPSVC is designed for the commonly used Gaussian kernel function. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the sensitive attribute values possessed by support vectors. By applying the PPSVC, the SVM classifier is able to be publicly released while preserving privacy. We prove that the PPSVC is robust against adversarial attacks. The experiments on real data sets show that the classification accuracy of the PPSVC is comparable to the original SVM classifier.

Discrimination Between Pathological and Normal Voices Using GMM-SVM Approach

Acoustic features of vocal tract function are used widely in the study of pathological voices detection. Classification of normal and pathological voices by acoustic parameters is a useful way to diagnose voice diseases. In this aspect, mel-frequency cepstral coefficients are proved to be effective with traditional classifiers such as Gaussian Mixture Model (GMM). However, the accuracy of the classification method can be further improved. In this article, a Gaussian mixture model supervector kernel-support vector machine (GMM-SVM) classifier is compared with GMM classifier for the detection of voice pathology. We found that a sustain vowel phonation can be classified as normal or pathological with an accuracy of 96.1%. Voice recordings are selected from the Kay database to carry out the experiments. Experimental results show that equal error rates decrease from 8.0% for GMM to 4.6% for GMM-SVM.