  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 for classifying text need sufficient documents
to learn accurately. This paper presents a new algorithm for text
classification using artificial intelligence technique 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 na\"ive 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.
