Open AccessMulti-class classification for large datasets with optimized SVM by non-linear kernel function
Author(s) -
Lingam Sunitha,
M. Bal Raju
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2089/1/012015
Subject(s) - support vector machine , kernel (algebra) , radial basis function kernel , computer science , artificial intelligence , pattern recognition (psychology) , polynomial kernel , kernel method , multiclass classification , binary classification , class (philosophy) , tree kernel , structured support vector machine , least squares support vector machine , machine learning , data mining , mathematics , combinatorics
Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.