Using genetic algorithms to optimize nearest neighbors for data mining |
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Authors: | Hyunchul Ahn Kyoung-jae Kim |
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Institution: | (1) Department of Business Administration, College of Social Sciences, Sungshin Women’s University, 249-1, Dongseon-Dong 3-Ga, Seongbuk-Gu, Seoul, 136-742, Republic of Korea;(2) Department of Management Information Systems, Dongguk University, 3-26 Pil-Dong, Chung-Gu, Seoul, 100-715, Republic of Korea |
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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. |
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Keywords: | Case-based reasoning Genetic algorithms Number of neighbors to combine Stock market prediction Purchase prediction |
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