Nugget Discovery with a Multi-objective Cultural Algorithm

TL;DR

This paper introduces a multi-objective cultural algorithm for nugget discovery, focusing on mining comprehensible classification rules that satisfy multiple user-defined properties, demonstrating promising results on benchmark datasets.

Contribution

It proposes a novel multi-objective cultural algorithm specifically designed for partial classification rule mining, addressing a research gap in this area.

Findings

Good performance on benchmark datasets

Effective multi-objective rule optimization

Enhanced rule comprehensibility

Abstract

Partial classification popularly known as nugget discovery comes under descriptive knowledge discovery. It involves mining rules for a target class of interest. Classification "If-Then" rules are the most sought out by decision makers since they are the most comprehensible form of knowledge mined by data mining techniques. The rules have certain properties namely the rule metrics which are used to evaluate them. Mining rules with user specified properties can be considered as a multi-objective optimization problem since the rules have to satisfy more than one property to be used by the user. Cultural algorithm (CA) with its knowledge sources have been used in solving many optimization problems. However research gap exists in using cultural algorithm for multi-objective optimization of rules. In the current study a multi-objective cultural algorithm is proposed for partial classification. Results of experiments on benchmark data sets reveal good performance.