TY - GEN
T1 - Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining
AU - Uwano, Fumito
AU - Takadama, Keiki
AU - Dobashi, Koji
AU - Kovacs, Tim
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/6
Y1 - 2018/7/6
N2 - 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.
AB - 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.
KW - Accuracy-based Learning Classifier System
KW - Data mining
KW - High-dimensional data
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85051458479&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051458479&partnerID=8YFLogxK
U2 - 10.1145/3205651.3208298
DO - 10.1145/3205651.3208298
M3 - Conference contribution
AN - SCOPUS:85051458479
T3 - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
SP - 1465
EP - 1472
BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -