概要

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.

論文誌情報

題目: Generalizing Rules by Random Forest-based Learning Classifier Systems for High-Dimensional Data Mining
著者: Fumito Uwano, Koji Dobashi, Keiki Takadama and Tim Kovacs
誌名: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO2018)
詳細: Kyoto, Japan, July 2018, pp. 1465-1472

Bibtex or Download

@inproceedings{fumito uwano 2018generalizing,
  title={Generalizing Rules by Random Forest-based Learning Classifier Systems for High-Dimensional Data Mining},
  author={Fumito Uwano and Koji Dobashi and Keiki Takadama and Tim Kovacs},
  booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO2018)},
  year={2018},
  pages={1465--1472},
  month={July}
}