Redundant Feature Selection Based on Hybrid GA and BPSO

Redundant Feature selection is an important topic in the field of bioinformatics. This paper proposes a novel algorithm on Redundant Feature Selection Based on Hybrid GA and BPSO(RFS-GSO), which tries to find a compact feature subset with great predictive ability. Compared with the previous works, RFS-GSO measures the redundancy of feature set by the maximum feature inter-correlation, which is more reasonable than those by the averaged inter-correlation. The outstanding performance of RFS-GSO has been examined by the experiments on several real world microarray data sets.

[1]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[2]  Zwe-Lee Gaing.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[3]  T. Poggio,et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[6]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[7]  Huan Liu,et al.  Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..

[8]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[9]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[10]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[12]  Edward R. Dougherty,et al.  Small Sample Issues for Microarray-Based Classification , 2001, Comparative and functional genomics.

[13]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[14]  S. Ramaswamy,et al.  Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. , 2002, Cancer research.

[15]  Chris H. Q. Ding,et al.  Evolving Feature Selection , 2005, IEEE Intell. Syst..

[16]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.