Adopting ant colony optimization for supervised text classification

Different electronic gadgets have become indispensable part of human life in the era of information technology, as a result abundant data is generated which is growing in exponential order. The data generated is generally stored in dumped repositories, with the sole purpose of verification as a proof. If the data is stored in classified repository, accessing the required data at a later time or navigation can be done easily. Treating the classified repository as a resource efficient decision making can be made easily. Ant Colony Optimization belongs to meta-heuristic class of optimization algorithms. An individual ant plays no role, but as a colony they are very powerful in solving optimization problems based on the probabilistic techniques. The paper attempts in classifying the textual documents using Ant Colony Optimization in supervised learning paradigm. The results obtained are encouraging.