A new approach to minimum attribute reduction based on discrete artificial bee colony

The minimum attribute reduction problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial optimization problem. In this paper, we propose an efficient and competitive combinatorial artificial bee colony algorithm for solving the minimum attribute reduction problem. In the proposed algorithm, a new multidimensional binary local search scheme for bee colonies based on velocity computation is presented; an employed bee and its recruited onlooker bees use different local search strategies so as to get a possibly more diversified neighboring search around a current food source; the information of the so-far best solution is exploited in various ways by employed bees, onlookers and scouts, respectively; the monotonicity property of classification quality of attribute subsets from the theory of rough sets is employed to avoid possibly invalid local searches. Performance comparisons with some best performing population-based metaheuristic algorithms for the minimum attribute reduction problem were carried out on a number of UCI data sets. The experimental results show that the proposed algorithm overall outperforms all the other algorithms in terms of solution quality and is therefore promising for solving the minimum attribute reduction problem.

[1]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[2]  Xu Zhang,et al.  An Attribute Reduction Algorithm Based on Clustering and Attribute-Activity Sorting , 2010, 2010 International Conference on Computational and Information Sciences.

[3]  Zuren Feng,et al.  An efficient ant colony optimization approach to attribute reduction in rough set theory , 2008, Pattern Recognit. Lett..

[4]  Dongyi Ye,et al.  A New Algorithm for Minimum Attribute Reduction Based on Binary Particle Swarm Optimization with Vaccination , 2007, PAKDD.

[5]  XIAOHUA Hu,et al.  LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[6]  Ali Husseinzadeh Kashan,et al.  DisABC: A new artificial bee colony algorithm for binary optimization , 2012, Appl. Soft Comput..

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

[8]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops , 2011, Inf. Sci..

[9]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[10]  Xiangyang Wang,et al.  Finding Minimal Rough Set Reducts with Particle Swarm Optimization , 2005, RSFDGrC.

[11]  Dongyi Ye,et al.  A novel and better fitness evaluation for rough set based minimum attribute reduction problem , 2013, Inf. Sci..

[12]  Narayana Prasad Padhy,et al.  Thermal unit commitment using binary/real coded artificial bee colony algorithm , 2012 .

[13]  Wei-Zhi Wu,et al.  Approaches to knowledge reductions in inconsistent systems , 2003, Int. J. Intell. Syst..

[14]  Weifeng Gao,et al.  A modified artificial bee colony algorithm , 2012, Comput. Oper. Res..

[15]  Tiranee Achalakul,et al.  The best-so-far selection in Artificial Bee Colony algorithm , 2011, Appl. Soft Comput..

[16]  Reza Akbari,et al.  A novel bee swarm optimization algorithm for numerical function optimization , 2010 .

[17]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[18]  Mohammad Shokouhifar,et al.  A discrete artificial bee colony for multiple Knapsack problem , 2013, Int. J. Reason. based Intell. Syst..

[19]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem , 2011, Inf. Sci..

[20]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[21]  Dervis Karaboga,et al.  A combinatorial Artificial Bee Colony algorithm for traveling salesman problem , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

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

[23]  Sanyang Liu,et al.  Improved artificial bee colony algorithm for global optimization , 2011 .

[24]  Krzysztof Krawiec,et al.  ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS , 1995, Comput. Intell..

[25]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[26]  Ben Niu,et al.  A Discrete Artificial Bee Colony Algorithm for TSP Problem , 2011, ICIC.

[27]  Ponnuthurai N. Suganthan,et al.  A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing , 2013, SEMCCO.

[28]  Derviş Karaboğa,et al.  NEURAL NETWORKS TRAINING BY ARTIFICIAL BEE COLONY ALGORITHM ON PATTERN CLASSIFICATION , 2009 .

[29]  Tiranee Achalakul,et al.  Job Shop Scheduling with the Best-so-far ABC , 2012, Eng. Appl. Artif. Intell..

[30]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[31]  Sushmita Mitra,et al.  Feature Selection Using Rough Sets , 2006, Multi-Objective Machine Learning.

[32]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[33]  Dusan Ramljak,et al.  Bee colony optimization for the p-center problem , 2011, Comput. Oper. Res..

[34]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[35]  Hao Zhang,et al.  An Artificial Bee Colony Algorithm Approach for Routing in VLSI , 2012, ICSI.

[36]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[37]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[38]  Masao Fukushima,et al.  Tabu search for attribute reduction in rough set theory , 2008, Soft Comput..

[39]  Witold Pedrycz,et al.  Analysis of alternative objective functions for attribute reduction in complete decision tables , 2011, Soft Comput..

[40]  Hai-Bin Duan,et al.  A Hybrid Artificial Bee Colony Optimization and Quantum Evolutionary Algorithm for Continuous Optimization Problems , 2010, Int. J. Neural Syst..

[41]  Rafael Bello,et al.  Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem , 2009 .

[42]  Guoyin Wang,et al.  Solving the Attribute Reduction Problem with Ant Colony Optimization , 2011, Trans. Rough Sets.

[43]  Dusan Ramljak,et al.  Bee colony optimization for scheduling independent tasks to identical processors , 2012, Journal of Heuristics.