Feature Selection based on manifold-learning with dynamic constraint handling differential evolution

Feature Selection in high dimensional feature space is the main challenge in statistic learning field. In this paper, a novel feature selection method based on manifold learning is proposed. The distance metric weight vector are optimized to maximize the multi-class margin in the manifold embedded in low dimension space, as well as minimize its L1-norm. This multi objectives optimization problem is solved by a Differential Evolution (DE) with dynamic constraint-handling mechanism. And a criterion to determine the best feature subset based on the optimal weight vector is given. The test result for selecting the optimal feature subset of UCI breast tissue dataset indicates that this real coded feature selection method could find some feature subset which has good classification robustness.

[1]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  N. Zaidi,et al.  SVMs and data dependent distance metric , 2010, 2010 25th International Conference of Image and Vision Computing New Zealand.

[3]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[4]  Hugues Bersini,et al.  A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Zhigang Shang,et al.  Differential evolution with dynamic constraint-handling mechanism , 2010, IEEE Congress on Evolutionary Computation.

[6]  Hong-Bo Xie,et al.  Ant colony optimization-based feature selection method for surface electromyography signals classification , 2012, Comput. Biol. Medicine.

[7]  Urszula Libal.  Feature selection for pattern recognition by LASSO and thresholding methods - a comparison , 2011, 2011 16th International Conference on Methods & Models in Automation & Robotics.

[8]  Zhigang Shang,et al.  Real Coded Feature Selection Integrated with Self-adaptive Differential Evolution Algorithm , 2012, ICIC.

[9]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[10]  Melba M. Crawford,et al.  Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning , 2014, IEEE Signal Processing Magazine.

[11]  Blaise Hanczar,et al.  Analysis of feature selection stability on high dimension and small sample data , 2014, Comput. Stat. Data Anal..

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

[13]  Jun Zhang,et al.  Evolutionary Computation Meets Machine Learning: A Survey , 2011, IEEE Computational Intelligence Magazine.

[14]  Nor Hayati Othman,et al.  A review of feature selection techniques via gene expression profiles , 2008, 2008 International Symposium on Information Technology.

[15]  Zijiang Yang,et al.  Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination , 2014, Expert Syst. Appl..