Please use this identifier to cite or link to this item: https://doi.org/10.1109/JPROC.2004.826605
Title: Computational intelligence methods for rule-based data understanding
Authors: Duch, W.
Setiono, R. 
Zurada, J.M.
Keywords: Data mining
Decision support
Decision trees
Feature selection
Fuzzy systems
Inductive learning
Logical rule extraction
Machine learning (ML)
Neural networks, neurofuzzy systems
Issue Date: 2004
Source: Duch, 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
Abstract: In 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.
Source Title: Proceedings of the IEEE
URI: http://scholarbank.nus.edu.sg/handle/10635/42611
ISSN: 00189219
DOI: 10.1109/JPROC.2004.826605
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