The primary goal of this research is to investigate the suitability of ant colony optimization, a swarm intelligence based meta-heuristic developed by mimicking some aspects of the food foraging behavior of ants, for building accurate and comprehensible classifiers which can be learned in reasonable time even for large datasets. Towards this end, a novel classification rule discovery algorithm called AntMiner-C and its variants are proposed. Various aspects and parameters of the proposed algorithms are investigated by experimentation on a number of benchmark datasets. Experimental results indicate that the proposed approach builds more accurate models when compared with commonly used classification algorithms. It is also computationally less expensive than previously available ant colony algorithm based classification rules discovery algorithms. A hybrid classifier using ant colony optimization is also proposed that combines association rules mining and supervised classification. Experiments show that the proposed algorithm has the ability to discover high quality rules. Furthermore, it has the advantage that association rules of each class can be mined in parallel if distributed processing is used. Experimental results demonstrate that the proposed hybrid classifier achieves higher accuracy rates when compared with other commonly used classification algorithms. A feature subset selection algorithm is also proposed which is based on ant colony optimization and decision trees. Experiments show that better accuracy is achieved if the subset of features selected by the proposed approach is used instead of full feature set and number of rules is also decreased substantially.