Data Mining using Advanced Ant Colony Optimization Algorithm and Application to Bankruptcy Prediction

Abstract

Ant Colony Optimization (ACO) is gaining popularity as data mining technique in the domain of Swarm Intelligence for its simple, accurate and comprehensive nature of classification. In this paper the authors propose a novel advanced version of the original ant colony based miner (Ant-Miner) in order to extract classification rules from data. They call this Advanced ACO-Miner (ADACOM). The main goal of ADACOM is to explore the flexibility of using a different knowledge extraction heuristic approach viz. Gini’s Index to increase the predictive accuracy and the simplicity of the rules extracted. Further, the authors increase the information and the prediction level of the set of rules extracted by dynamically changing specific parameters. Simulations are performed with ADACOM on a few benchmark datasets Wine, WBC (Wisconsin Breast Cancer) and Iris from UCI (University of California at Irvine) data repository and compared with Ant-Miner (Parpinelli, Lopes, & Freitas, 2002), Ant-Miner2 (Liu, Abbass, & McKay, 2002), Ant-Miner3 (Liu, Abbass, & McKay, 2003), Ant-Miner+ (Martens, De Backer, Haesen, Vanthienen, Snoeck, & Baesens, 2007) and C4.5 (Quinlan, 1993). The results show that ADACOM outperforms the above mentioned algorithms in terms of predictive accuracy, simplicity of rules, sensitivity, specificity and AUC values (area under ROC curve). In addition, the ADACOM is also employed to extract rules from bank datasets (UK, US, Spanish and Turkish) for bankruptcy prediction and the results are compared with that obtained by Ant-Miner. Again ADACOM yielded better results and is proven to be the better choice for solving bankruptcy prediction problems in banks