Extracting useful rules through improved decision tree induction using information entropy

TL;DR

This paper enhances decision tree induction by integrating attribute relevance, concept hierarchies, and a novel HeightBalancePriority algorithm to improve classification efficiency and rule extraction in large databases.

Contribution

It introduces a new decision tree construction method combining AOI, relevance analysis, and HeightBalancePriority, improving over C4.5 for large-scale data mining.

Findings

Improved decision trees with better accuracy and interpretability.

Enhanced scalability and efficiency in large datasets.

Effective rule extraction using the proposed method.

Abstract

Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchys knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.