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Publication - Dr Tim Kovacs

    Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining

    Citation

    Uwano, F, Takadama, K, Dobashi, K & Kovacs, T, 2018, ‘Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining’. in: GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion . Association for Computing Machinery (ACM), pp. 1465-1472

    Abstract

    This paper proposes high-dimensional data mining technique by integrating two data mining methods: Accuracy-based Learning Classifier Systems (XCS) and Random Forests (RF). Concretely, the proposed system integrates RF and XCS: RF generates several numbers of decision trees, and XCS generalizes the rules converted from the decision trees. The convert manner is as follows: (1) the branch node of the decision tree becomes the attribute; (2) if the branch node does not exist, the attribute of that becomes # for XCS; and (3) One decision tree becomes one rule at least. Note that # can become any value in the attribute. From the experiments of Multiplexer problems, we derive that: (i) the good performance of the proposed system; and (ii) RF helps XCS to acquire optimal solutions as knowledge by generating appropriately generalized rules.

    Full details in the University publications repository