19
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Integration of Soft Computing Approaches for Feature Subset Selection

Pages 403-414 | Published online: 26 Mar 2015

REFERENCES

  • P A Devijar & J Kittler, Pattern Recognition: A Statistical Approach, Prentice Hall International, 1982.
  • K Fukunaga, Introduction to Statistical Pattern Recognition, Academic press, 1990.
  • R Duda & P Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1993.
  • C M Bishop, Neural Networks for Pattern Recognition, Oxford University press, 1995.
  • LA Zadeh et al. Fuzzy Sets and their Application to Cognitive and Decision Processes, Academic Press, 1975.
  • D E Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
  • J H Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor. 1975.
  • R K De, J Basak & S K Pal, Neuro-fuzzy fature evaluation with theoretical analysis, Neural Networks, vol 12, pp 1429–1455, 1999.
  • S K Pal, R K De & J Basak, Unsupervised feature evaluation: A neuro-fuzzy approach, IEEE Trans on Neural Networks, vol 11, pp 366–376, 2000.
  • J Basak, R K De & S K Pal, Unsupervised feature selection using neurofuzzy approach. Pattern Recognition Letters, vol 19, pp 997–1006, 1998.
  • H Almuallim & T G Dietterich, Learning boolean concepts in the presence of many irrelevant features, Artificial Intelligence, vol 69. pp 279–305. 1994.
  • K Kira & L A Rendell. The feature selection problem: Traditional methods and a new algorithm. Proceedings of Ninth National Conference on Artificial Intelligence, pp 129–134, MIT Press. 1992.
  • G H John, R Kohavi & K Pfleeger, Irrelevant feature and the subset selection problem. Machine Learning: Proceedings of the Eleventh International Conference, pp 121–129, 1994.
  • D W Ruck, S K Rogers & M Kabrisky. Feature Selection Using a Multilayer Perceptron, Neural Network Computation, vol 20, pp 40–48, Fall 1990.
  • G D Garson, Interpreting Neural Network Connection Weights, AI Expert, pp 47–51, 1991.
  • G Tarr, Multi-layered feedforward neural networks for image segmentation. PhD dissertation prospectus. School of Engineering, Air Force Institute of Technology. Wright-Patterson A FB OH, 1991.
  • L M Belue & K W Bauer, Determining input features for multilayer perceptrons, Neurocomputing, vol 7, no 2, pp 111–121, March 1995.
  • K M Steppe & K W Bauer, Jr Improved feature screening in feedforward neural networks. Neurocomputing, vol 13, pp 47–58, 1996.
  • R Setiono & H Liu, Neural-Network Feature Selector, IEEE Trans on NN, vol 8, no 3, pp 654–662. May 1997.
  • Y Le Cun, J S Denker & S A Solla, Optimal brain damage, in Advances in Neural Information processing Systems, D S Touretzky (Ed), vol 2, pp 598–605, 1990.
  • R Reed. Pruning Algorithms—A survey, IEEE Trans on NN, vol 4, pp 740–747, September, 1993.
  • W Siedlecki & J Sklansky, A note on genetic algorithms for large scale feature selection. Pattern Recognition Letter, vol 10. pp 335–347, 1989.
  • J Yang & V Honavar, Feature Subset Selection using a Genetic Algorithm, Feature Extraction, Construction and Selection: A data Mining Perspective (Eds), pp 117–136, Kluwer Academic Publishers, 1998.
  • Y K Wang & K C Fan, Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey, In Proceedings of ICPR'96, pp 740–744, 1996.
  • H Vafaie & K de Jong, Genetic Algorithms as a tool for Feature Selection in Machine Learning, In Proceedings of the 4th International Conference on Tools with Artificial Intelligence, November, 1992.
  • H Vafaie & K de Jong, Genetic Algorithms as a Tool for restructuring Feature Space Representations, In Proceedings of the 7th International Conference on Tools with Artificial Intelligence, 1995.
  • K J Cherkaur & J W Shavlik, Growing Simpler Decision Trees to facilitate Knowledge Discovery, In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pp 315–318, 1996.
  • J D Kelly & L Davis, Hybridizing the Genetic Algorithm and the k-nearest Neighbours Classification Algorithm, Proceedings of the 4th International Conference on genetic Algorithms and their Applications, pp 377–383, 1991.
  • W F Punch et al. Further Research on Feature selection and Classification Using Genetic Algorithms, Proceedings of 5th International Conference on genetic Algorithms and their Applications, pp 557–564, 1993.
  • F Z Brill, D E Brown & W N Martin, Fast Genetic Selection of Features for Neural Network Classifiers, IEEE Trans on NN, vol 3, no 2, pp 324–328. March 1992.
  • M J Martin-Bautista & M A Vila, Applying Genetic Algorithms to the Feature Selection Problem in Information Retrieval, Lecture Notes in Artificial Intelligence, Springer Verlog. 1998.
  • Basabi Chakraborty & Yasuji Sawada, Fractal Neural Network Feature Selector for Automatic Pattern recognition System, IEICE Trans on Fundamentals of Electronics, Communications and Computer Science, vol E82-A, no 9, pp 1845–1850, September 1999.
  • B B Mandelbrot, The fractal Geometry of Nature. Freeman, San Francisco, CA 1982.
  • Basabi Chakraborty & Goutam Chakraborty, A Neuro Fuzzy Algorithm for Feature Subset Selection, IEICE Trans on Fundamentals of Electronics, Communications and Computer Sciences, vol E84-A, no 9. September 2001.
  • S K Pal & Basabi Chakraborty, Fuzzy set Theoretic Measure for Automatic Feature Selection, IEEE Trans on SMS, vol SMX-16, no 5, pp 754–760, Sep/Oct 1986.
  • A Deluca & S Termini, A definition of a nonprobabilistic entropy in the setting of a Fuzzy Sets Theory, Information and Control, vol 20, pp 301–312, 1972.
  • B Chakraborty & Y Sawada, Feature Subset Evaluation using Fuzzy Measures, Proceeding of ANZIIS'95, pp 220–225, November, 1995.
  • J C Bezdek, Pattern recognition with Fuzzy Objective Fnctions, Plenum Press, NY, 1981.
  • P R Gorman & T J Sejnowski, Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets, Neural Networks, vol 1, pp75–89, 1988.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.