Enhanced Whale Optimization Technique-Based DT Algorithm for Calculating Homogeneous Samples using Entropy

Abstract

A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogeneous). Algorithm uses entropy to calculate the homogeneity of a sample. If the sample is completely homogeneous the entropy is zero and if the sample is equally divided then it has entropy of one.Whale Optimization Technique based decision tree which is used to improve their efficiency for handle the feature prediction. Because it provides high precision and quick classification.Whale optimization operators such as encircling features, search for features, and hunting features. Collect the student dataset from online. It contains many features about student. The first operator performs encircled the available features and after collect the optimized features based on widely used in educational mining. In this paper, the predictive performance of DT algorithm is enhanced by proposing the whale optimization technique that will handle the stepwise features prediction and even present a simplest calculation of entropy and information gain.