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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/104256

    Title: Using the group genetic algorithm to improve performance of attribute clustering
    Authors: Hong, T. P.;Chen, C. H.;Lin, F. S.
    Keywords: Attribute clustering;Feature selection;Genetic algorithm;Grouping genetic algorithm;Data mining
    Date: 2015-04-01
    Issue Date: 2016-01-06 10:53:10 (UTC+8)
    Abstract: Feature selection is a pre-processing step in data mining and machine learning, and is very important in analyzing high-dimensional data. Attribute clustering has been proposed for feature selection. If similar attributes can be clustered into groups, they can then be easily replaced by others in the same group when some attribute values are missing. Hong et al. proposed a genetic algorithm (GA) to find appropriate attribute clusters. However, in their approaches, multiple chromosomes represent the same attribute clustering result (feasible solution) due to the combinatorial property, and thus the search space is larger than necessary. This study improves the performance of the GA-based attribute clustering process based on the grouping genetic algorithm (GGA). In the proposed approach, the general GGA representation and operators are used to reduce redundancy in the chromosome representation for attribute clustering. Experiments are also conducted to compare the efficiency of the proposed approach with that of an existing approach. The results indicate that the proposed approach can derive attribute grouping results in an effective way.
    Relation: Applied Soft Computing 29, pp.371–378
    DOI: 10.1016/j.asoc.2015.01.001
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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