Support vector machine classification using semi-parametric model

AuthorsMohammad Ghasem Akbari,Saeed Khorashadizadeh,Mohammadhassan Majidi
JournalSoft Computing
Page number10049-10062
Serial number26
Volume number19
Paper TypeFull Paper
Published At2022
Journal GradeISI
Journal TypeTypographic
Journal CountryBelgium
Journal IndexISI،JCR،Scopus

Abstract

Pattern recognition and data mining using support vector machine (SVM) have been the focus of widespread researches in recent decades. In SVM, a hyper-plane is designed to classify the training data. A challenge in SVM is that the parameters of hyper-planes are constants. As a result, there may be some critical points that will be classified into a wrong set. It should be mentioned that finding this hyper-plane is very similar to solving a regression problem using parametric or semi-parametric models in statistics. This is the main motivation of this paper. The contribution of this paper is combining SVM classifier and semi-parametric models (SP-SVM) to solve the aforementioned challenge. In fact, using semi-parametric linear model results in some serial linear decision boundaries with several slopes and intercepts. In other words, there are two types of kernels in the proposed SP-SVM: the kernels that perform nonlinear transformation of the input features and the kernels needed in the semi-parametric model. The validations have been done on Iris data set and also some other linearly non-separable classification problems. The accuracy of the proposed SP-SVM outperforms some related algorithms such as K-nearest neighbor (KNN)-based weighted multi-class twin support vector machines (KWMTSVM), support vector classification–regression machine for K-class classification (K-SVCR), twin multi-class classification support vector machines (twin-KSVC), intelligent particle swarm classifier (IPS-classifier) and random forest. The accuracy of SP-SVM is 97.33%. Thus, SP-SVM can play an important role in increasing the accuracy of industrial machines that perform classifications, for example, agricultural products.

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tags: Support vector machine _ Semi-parametric linear regression _ Decision boundary