Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. Three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). The AMMLP is a new method applied for classification of patterns. The results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.

[1]  Di Xiao,et al.  Importance Degree of Features and Feature Selection , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[2]  Byung Ro Moon,et al.  Hybrid Genetic Algorithms for Feature Selection , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Diego Andina,et al.  Artificial Metaplasticity can Improve Artificial Neural Networks Learning , 2013, Intell. Autom. Soft Comput..

[4]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[5]  Emilio Del-Moral-Hernandez,et al.  A Preliminary Neural Model for Movement Direction Recognition Based on Biologically Plausible Plasticity Rules , 2007, IWINAC.

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

[7]  Beatrice Lazzerini,et al.  Feature Selection based on Similarity , 2002 .

[8]  D. Andina,et al.  Wood defects classification using Artificial Metaplasticity neural network , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[9]  David Casasent,et al.  An improvement on floating search algorithms for feature subset selection , 2009, Pattern Recognit..

[10]  Abraham Kandel,et al.  Information-theoretic algorithm for feature selection , 2001, Pattern Recognit. Lett..

[11]  Martin T. Hagan,et al.  Neural network design , 1995 .

[12]  Witold Pedrycz,et al.  Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Chih-Ming Chen,et al.  An efficient fuzzy classifier with feature selection based on fuzzy entropy , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[14]  M. Bear,et al.  Metaplasticity: the plasticity of synaptic plasticity , 1996, Trends in Neurosciences.

[15]  D. Andina,et al.  Using neural networks to simulate the Alzheimer’s Disease , 2008, 2008 World Automation Congress.

[16]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[17]  Weiguo Sheng,et al.  A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection , 2008, IEEE Transactions on Knowledge and Data Engineering.

[18]  Henry Leung,et al.  The complex backpropagation algorithm , 1991, IEEE Trans. Signal Process..

[19]  Nikhil R. Pal,et al.  Genetic programming for simultaneous feature selection and classifier design , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Wickliffe C. Abraham,et al.  Metaplasticity: Key Element in Memory and Learning? , 1999, News in physiological sciences : an international journal of physiology produced jointly by the International Union of Physiological Sciences and the American Physiological Society.

[21]  R. Potolea,et al.  Improving classification accuracy through feature selection , 2008, 2008 4th International Conference on Intelligent Computer Communication and Processing.

[22]  Shyi-Ming Chen,et al.  A New Method for Feature Subset Selection for Handling Classification Problems , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[23]  Gerhard Rigoll,et al.  Selecting Features in On-Line Handwritten Whiteboard Note Recognition: SFS or SFFS? , 2009, 2009 10th International Conference on Document Analysis and Recognition.