Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of “on condition” diagnostic techniques in complex systems, such as the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features have been shown to unnecessarily increase the complexity of the classification problem and degrade the diagnostic performance.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Luiz Eduardo Soares de Oliveira,et al.  Feature selection for ensembles:a hierarchical multi-objective genetic algorithm approach , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[3]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Riyaz Sikora,et al.  Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance , 2005, Inf. Technol. Manag..

[5]  Gustavo Alonso,et al.  BWR online monitoring system based on noise analysis , 2006 .

[6]  Flávio Bortolozzi,et al.  Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[7]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[10]  Marcel Rijckaert,et al.  Scalars, a way to improve the multi-objective prediction of the GAdC-method , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[11]  Fakhri Karray,et al.  Distributed Genetic Algorithm with Bi-Coded Chromosomes and a New Evaluation Function for Features Selection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Larry Bull,et al.  Genetic Programming with a Genetic Algorithm for Feature Construction and Selection , 2005, Genetic Programming and Evolvable Machines.

[13]  Davide Roverso.  Fault diagnosis with the Aladdin transient classifier , 2003, SPIE Defense + Commercial Sensing.

[14]  K. I. Ramachandran,et al.  Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing , 2007 .

[15]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[16]  Hirotaka Nakayama,et al.  Theory of Multiobjective Optimization , 1985 .

[17]  Xue-wen Chen.  An improved branch and bound algorithm for feature selection , 2003, Pattern Recognit. Lett..

[18]  C. McGreavy,et al.  Application of wavelets and neural networks to diagnostic system development, 2, an integrated framework and its application , 1999 .

[19]  Davide Roverso.  Soft computing tools for transient classification , 2000, Inf. Sci..

[20]  Ravi Kothari,et al.  Feature subset selection using a new definition of classifiability , 2003, Pattern Recognit. Lett..

[21]  D.A. Stacey,et al.  Feature subset selection via multi-objective genetic algorithm , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[22]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[23]  Marco Laumanns,et al.  Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization , 2002, GECCO.

[24]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[25]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[26]  Alessandra Russo,et al.  Multistyle classification of speech under stress using feature subset selection based on genetic algorithms , 2007, Speech Commun..

[27]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[28]  Xiaoming Xu,et al.  A hybrid genetic algorithm for feature selection wrapper based on mutual information , 2007, Pattern Recognit. Lett..

[29]  P. Baraldi,et al.  Selecting features for nuclear transients classification by means of genetic algorithms , 2006, IEEE Transactions on Nuclear Science.

[30]  Jonathan E. Fieldsend,et al.  Using unconstrained elite archives for multiobjective optimization , 2003, IEEE Trans. Evol. Comput..

[31]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[32]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[33]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[34]  Luiz Eduardo Soares de Oliveira,et al.  Unsupervised feature selection for ensemble of classifiers , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[35]  Martina Gorges-Schleuter,et al.  Application of Genetic Algorithms to Task Planning and Learning , 1992, Parallel Problem Solving from Nature.

[36]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[37]  Sung-Bae Cho,et al.  Efficient huge-scale feature selection with speciated genetic algorithm , 2005 .

[38]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[39]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[40]  Belle R. Upadhyaya,et al.  Failure Detection Using a Fuzzy Neural Network with an Automatic Input Selection Algorithm , 2002 .

[41]  Riyaz Sikora,et al.  Framework for efficient feature selection in genetic algorithm based data mining , 2007, Eur. J. Oper. Res..

[42]  Andrew Hunter,et al.  Selecting features in neurofuzzy modelling by multiobjective genetic algorithms , 1999 .

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

[44]  Brijesh Verma,et al.  Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection , 2005, Pattern Recognit. Lett..

[45]  Stanisław Radkowski,et al.  Geometrical method of selection of features of diagnostic signals , 2007 .

[46]  Hongbin Zhang,et al.  Feature selection using tabu search method , 2002, Pattern Recognit..

[47]  José Pedro Santos,et al.  Analysis of neural networks and analysis of feature selection with genetic algorithm to discriminate among pollutant gas , 2004 .

[48]  R. Schirru,et al.  GENETIC BASED TRANSIENT IDENTIFICATION SYSTEM DESIGN WITH AUTOMATIC SELECTION OF MEANINGFUL VARIABLES , 2002 .

[49]  Kalyanmoy Deb,et al.  Massive Multimodality, Deception, and Genetic Algorithms , 1992, PPSN.

[50]  Fakhri Karray,et al.  Multi-objective Feature Selection with NSGA II , 2007, ICANNGA.

[51]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[52]  C. McGreavy,et al.  Application of wavelets and neural networks to diagnostic system development , 1999 .

[53]  Hamparsum Bozdogan.  Intelligent Statistical Data Mining with Information Complexity and Genetic Algorithms Hamparsum Bozdogan University of Tennessee, Knoxville, USA , 2003 .

[54]  Shi Zhong-ke,et al.  Overview of multi-objective optimization methods , 2004 .

[55]  Lance D. Chambers.  Practical handbook of genetic algorithms , 1995 .

[56]  Francesco Marcelloni,et al.  Feature selection based on a modified fuzzy C-means algorithm with supervision , 2003, Inf. Sci..

[57]  Gary G. Yen,et al.  Rank-density-based multiobjective genetic algorithm and benchmark test function study , 2003, IEEE Trans. Evol. Comput..

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

[59]  Hamparsum Bozdogan,et al.  Statistical Data Mining and Knowledge Discovery , 2004 .

[60]  Takashi Sato,et al.  Basic concept of a near future BWR , 2004 .

[61]  Enrico Zio,et al.  An extended classifiability index for feature selection in nuclear transients , 2005 .

[62]  Guy Clerc,et al.  The use of features selection and nearest neighbors rule for faults diagnostic in induction motors , 2006, Eng. Appl. Artif. Intell..

[63]  Luiz Eduardo Soares de Oliveira,et al.  Feature selection using multi-objective genetic algorithms for handwritten digit recognition , 2002, Object recognition supported by user interaction for service robots.

[64]  G. Yen,et al.  Fault classification on vibration data with wavelet based feature selection scheme , 2006, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[65]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[66]  Antanas Verikas,et al.  Feature selection with neural networks , 2002, Pattern Recognit. Lett..