Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers

Artificial Life and Robotics

Volumn 13, Issue 1, 2008, Pages 294-297

  Kuwajima, Isao ^a   Nojima, Yusuke ^a   Ishibuchi, Hisao ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

Fuzzy sets; Genetic rule selection; Pattern classification

Indexed keywords

FUZZY SETS; LEARNING SYSTEMS;

COMPUTATIONAL EXPERIMENTS; CONTINUOUS ATTRIBUTES; DISCRETE NUMBERS; DISCRETIZATION; DISCRETIZATIONS; FUZZIFICATION; GENERALIZATION ABILITIES; GENETIC RULE SELECTION; PATTERN CLASSIFICATION; TRAINING CLASSIFIERS;

CLASSIFIERS;

EID: 58049205744     PISSN: 14335298     EISSN: 16147456     Source Type: Journal    
DOI: 10.1007/s10015-008-0515-7     Document Type: Article

References (5)

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2. Ishibuchi H, Yamamoto T (2002) Performance evaluation of fuzzy partitions with different fuzzification grades. In: Proceedings of 2002 IEEE International Conference on Fuzzy Systems, pp 1198-1203
3. Hong TP, Kuo CS, Chi SC (2001) Tradeoff between computation time and number of rules for fuzzy mining from quantitative data. Intl J Uncertainty, Fuzziness and Knowledge-Based Systems 9(6): 587-604
4. Ishibuchi H, Kuwajima I, Nojima Y (2007) Relation between Pareto-optimal fuzzy rules and Pareto-optimal fuzzy rule sets. In: Proceedings of IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp 42-49
5. Elomaa T, Rousu J (1999) General and efficient multisplitting of numerical attributes. Machine Learning 36(3):201-244