Please use this identifier to cite or link to this item: https://doi.org/10.1109/JPROC.2004.826605
DC FieldValue
dc.titleComputational intelligence methods for rule-based data understanding
dc.contributor.authorDuch, W.
dc.contributor.authorSetiono, R.
dc.contributor.authorZurada, J.M.
dc.date.accessioned2013-07-11T10:13:40Z
dc.date.available2013-07-11T10:13:40Z
dc.date.issued2004
dc.identifier.citationDuch, W., Setiono, R., Zurada, J.M. (2004). Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE 92 (5) : 771-805. ScholarBank@NUS Repository. https://doi.org/10.1109/JPROC.2004.826605
dc.identifier.issn00189219
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42611
dc.description.abstractIn many applications, black-box prediction is not satisfactory, and understanding the data is of critical importance. Typically, approaches useful for understanding of data involve logical rules, evaluate similarity to prototypes, or are based on visualization or graphical methods. This paper is focused on the extraction and use of logical rules for data understanding. All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the rule-extraction stage, and tradeoffs between rejection and error level at the rule optimization stage. Stability of rule-based description, calculation of probabilities from rules, and other related issues are also discussed. Major approaches to extraction of logical rules based on neural networks, decision trees, machine learning, and statistical methods are introduced. Optimization and application issues for sets of logical rules are described. Applications of such methods to benchmark and real-life problems are reported and illustrated with simple logical rules for many datasets. Challenges and new directions for research are outlined. © 2004 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/JPROC.2004.826605
dc.sourceScopus
dc.subjectData mining
dc.subjectDecision support
dc.subjectDecision trees
dc.subjectFeature selection
dc.subjectFuzzy systems
dc.subjectInductive learning
dc.subjectLogical rule extraction
dc.subjectMachine learning (ML)
dc.subjectNeural networks, neurofuzzy systems
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1109/JPROC.2004.826605
dc.description.sourcetitleProceedings of the IEEE
dc.description.volume92
dc.description.issue5
dc.description.page771-805
dc.description.codenIEEPA
dc.identifier.isiut000220997800002
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