Title :
Study of cost-sensitive ant colony data mining algorithm
Author :
Song, DingLi ; Yang, Bingru ; Peng, Zhen ; Fang, Weiwei
Author_Institution :
Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Abstract :
Recently, cost-sensitive data mining has been an area of extensive research interests. Intelligent ant colony classification algorithm is introduced in cost-sensitive data mining method in order to obtain satisfied classification results by interaction of ant individuals. The convergence rate of classification is increased by using of metacost´s meta-learning theory. Moreover, Boosting theory is investigated to improve the classification results. As a result, some satisfied performances can be obtained by the combination of above three theories.
Keywords :
convergence; data mining; learning (artificial intelligence); optimisation; pattern classification; BMCAM algorithm; Boosting metacost-for-cost-sensitive ant miner; convergence rate; cost-sensitive data mining algorithm; intelligent ant colony classification algorithm; meta-learning theory; Boosting; Classification algorithms; Data engineering; Data mining; Educational institutions; Electronic mail; Evolutionary computation; Learning systems; Particle swarm optimization; Probability distribution; Boosting theory; ant colony algorithm; cost-sensitive;
Conference_Titel :
Industrial Mechatronics and Automation, 2009. ICIMA 2009. International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-3817-4
DOI :
10.1109/ICIMA.2009.5156670
