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

fYear

2009

fDate

15-16 May 2009

Firstpage

488

Lastpage

491

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Mechatronics and Automation, 2009. ICIMA 2009. International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-3817-4

Type

conf

DOI

10.1109/ICIMA.2009.5156670

Filename

5156670