Text Classification using Data Mining

TL;DR

This paper introduces a novel text classification algorithm that leverages data mining, association rules, Naive Bayes, and genetic algorithms to improve accuracy with fewer training documents.

Contribution

It proposes a new method combining association rules, Naive Bayes, and genetic algorithms for effective text classification requiring less training data.

Findings

System successfully classifies texts with fewer documents

Uses association rules for feature extraction

Integrates Naive Bayes and genetic algorithms

Abstract

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.