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Exploration of a hybrid feature selection algorithm

Author

Listed:
  • K-M Osei-Bryson

    (Virginia Commonwealth University)

  • K Giles

    (Virginia Commonwealth University)

  • B Kositanurit

    (Virginia Commonwealth University)

Abstract

In the Knowledge Discovery Process, classification algorithms are often used to help create models with training data that can be used to predict the classes of untested data instances. While there are several factors involved with classification algorithms that can influence classification results, such as the node splitting measures used in making decision trees, feature selection is often used as a pre-classification step when using large data sets to help eliminate irrelevant or redundant attributes in order to increase computational efficiency and possibly to increase classification accuracy. One important factor common to both feature selection as well as to classification using decision trees is attribute discretization, which is the process of dividing attribute values into a smaller number of discrete values. In this paper, we will present and explore a new hybrid approach, ChiBlur, which involves the use of concepts from both the blurring and χ 2-based approaches to feature selection, as well as concepts from multi-objective optimization. We will compare this new algorithm with algorithms based on the blurring and χ 2-based approaches.

Suggested Citation

  • K-M Osei-Bryson & K Giles & B Kositanurit, 2003. "Exploration of a hybrid feature selection algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(7), pages 790-797, July.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:7:d:10.1057_palgrave.jors.2601565
    DOI: 10.1057/palgrave.jors.2601565
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    References listed on IDEAS

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    1. N Bryson & A Joseph, 2001. "Optimal techniques for class-dependent attribute discretization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(10), pages 1130-1143, October.
    2. Selwyn Piramuthu, 1999. "Feature Selection for Financial Credit-Risk Evaluation Decisions," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 258-266, August.
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