A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization

Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

[1]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[2]  Yiyu Yao,et al.  Rough Sets and Current Trends in Computing , 2001, Lecture Notes in Computer Science.

[3]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[4]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[6]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[7]  Qiang Shen,et al.  A Rough Set-Aided System for Sorting WWW Bookmarks , 2001, Web Intelligence.

[8]  Ajith Abraham,et al.  Nature Inspired Population-Based Heuristics for Rough Set Reduction , 2009 .

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[11]  Zdzisław Pawlak,et al.  Rough sets. Present state and the future , 1993 .

[12]  Qiang Shen,et al.  Rough set-aided keyword reduction for text categorization , 2001, Appl. Artif. Intell..

[13]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[14]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[15]  Zdzislaw Pawlak,et al.  Rough sets and intelligent data analysis , 2002, Inf. Sci..

[16]  Qiang Shen,et al.  Finding Rough Set Reducts with Ant Colony Optimization , 2003 .

[17]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[18]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..