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Near-optimal feature selection for large databases

Author

Listed:
  • J Yang

    (Chonbuk National University)

  • S Ólafsson

    (Iowa State University)

Abstract

We analyse a new optimization-based approach for feature selection that uses the nested partitions method for combinatorial optimization as a heuristic search procedure to identify good feature subsets. In particular, we show how to improve the performance of the nested partitions method using random sampling of instances. The new approach uses a two-stage sampling scheme that determines the required sample size to guarantee convergence to a near-optimal solution. This approach therefore also has attractive theoretical characteristics. In particular, when the algorithm terminates in finite time, rigorous statements can be made concerning the quality of the final feature subset. Numerical results are reported to illustrate the key results, and show that the new approach is considerably faster than the original nested partitions method and other feature selection methods.

Suggested Citation

  • J Yang & S Ólafsson, 2009. "Near-optimal feature selection for large databases," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1045-1055, August.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_palgrave.jors.2602651
    DOI: 10.1057/palgrave.jors.2602651
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    References listed on IDEAS

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    1. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    2. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
    3. Sigurdur Ólafsson & Jaekyung Yang, 2005. "Intelligent Partitioning for Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 339-355, August.
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