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Using genetic algorithms to optimize nearest neighbors for data mining

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  • Hyunchul Ahn
  • Kyoung-jae Kim

Abstract

Case-based reasoning (CBR) is widely used in data mining for managerial applications because it often shows significant promise for improving the effectiveness of complex and unstructured decision making. There are, however, some limitations in designing appropriate case indexing and retrieval mechanisms including feature selection and feature weighting. Some of the prior studies pointed out that finding the optimal k parameter for the k-nearest neighbor (k-NN) is also one of the most important factors for designing an effective CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. This study proposes a genetic algorithm (GA) approach to optimize the number of neighbors to combine. In this study, we apply this novel model to two real-world cases involving stock market and online purchase prediction problems. Experimental results show that a GA-optimized k-NN approach may outperform traditional k-NN. In addition, these results also show that our proposed method is as good as or sometime better than other AI techniques in performance-comparison. Copyright Springer Science+Business Media, LLC 2008

Suggested Citation

  • Hyunchul Ahn & Kyoung-jae Kim, 2008. "Using genetic algorithms to optimize nearest neighbors for data mining," Annals of Operations Research, Springer, vol. 163(1), pages 5-18, October.
    • Handle: RePEc:spr:annopr:v:163:y:2008:i:1:p:5-18:10.1007/s10479-008-0325-2
    DOI: 10.1007/s10479-008-0325-2
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    Cited by:

    1. Andrew Kusiak & Xiupeng Wei, 2014. "Prediction of methane production in wastewater treatment facility: a data-mining approach," Annals of Operations Research, Springer, vol. 216(1), pages 71-81, May.
    2. Konstantin Kogan & Avi Herbon & Beatrice Venturi, 2020. "Direct marketing of an event under hazards of customer saturation and forgetting," Annals of Operations Research, Springer, vol. 295(1), pages 207-227, December.
    3. Şenay Yaşar Sağlam & W. Nick Street, 2018. "Distant diversity in dynamic class prediction," Annals of Operations Research, Springer, vol. 263(1), pages 5-19, April.
    4. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.

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