Multi-objective PSO Algorithm for Feature Selection Problems with Unreliable Data

Feature selection is an important data preprocessing technique in classification problems. This paper focuses on a new feature selection problem, in which sampling data of different features have different reliability degree. First, the problem is modeled as a multi-objective optimization. There two objectives should be optimized simultaneously: reliability and classifying accuracy of feature subset. Then, a multi-objective feature selection method based on particle swarm optimization, called JMOPSO, is proposed by incorporating several effective operators. Finally, experimental results suggest that the proposed JMOPSO is a highly competitive feature selection method for solving the feature selection problem with unreliable data.

[1]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Mohammad Majid al-Rifaie,et al.  Bare Bones Particle Swarms with Jumps , 2012, ANTS.

[4]  Magdalene Marinaki,et al.  A Hybridized Particle Swarm Optimization with Expanding Neighborhood Topology for the Feature Selection Problem , 2013, Hybrid Metaheuristics.

[5]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Mengjie Zhang,et al.  A multi-objective particle swarm optimisation for filter-based feature selection in classification problems , 2012, Connect. Sci..

[8]  Pa-Chun Wang,et al.  Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis , 2011, Neural Computing and Applications.

[9]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[10]  Li-Yeh Chuang,et al.  Chaotic maps based on binary particle swarm optimization for feature selection , 2011, Appl. Soft Comput..

[11]  Chao-Ton Su,et al.  Integrated fuzzy-connective-based aggregation network with real-valued genetic algorithm for quality of life evaluation , 2011, Neural Computing and Applications.

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

[13]  Christian Blum,et al.  Hybrid Metaheuristics , 2010, Artificial Intelligence: Foundations, Theory, and Algorithms.

[14]  Renato A. Krohling,et al.  Bare Bones Particle Swarm Optimization with Gaussian or Cauchy jumps , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Hao Dong,et al.  An improved particle swarm optimization for feature selection , 2011 .

[16]  Tim Blackwell,et al.  A Study of Collapse in Bare Bones Particle Swarm Optimization , 2012, IEEE Transactions on Evolutionary Computation.