A New Method for Classification of Datasets for Data Mining

TL;DR

This paper introduces an improved decision tree algorithm that addresses ID3's tendency to favor attributes with many values, enhancing classification accuracy and efficiency.

Contribution

The paper proposes a novel attribute grouping method and selection measure to improve decision tree classification over ID3.

Findings

More accurate data set classification

Enhanced efficiency in decision tree construction

Better handling of attributes with many values

Abstract

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.