A comparative study of classification techniques: Support vector machine, fuzzy support vector machine & decision trees
Priyank Pandey, Amita Jain · International Conference on Computing for Sustainable Global Development · 2016
A support vector machine (SVM) is a classification technique that learns the decision surface from two different classes of the input points. There are many cases where these input points cannot be assigned to one specific class. The fuzzy support vector machine (FSVM) was proposed for this purpose. One another classification technique is decision tree. In this paper we are doing a comparative study of these three classification methods. We are doing comparison in terms of inputs and the produced output.