Journal
PROCEEDINGS OF THE IEEE
Volume 92, Issue 5, Pages 771-805Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2004.826605
Keywords
data mining; decision support; decision trees; feature selection; fuzzy systems; inductive learning; logical rule extraction; machine learning (ML); neural networks; neurofuzzy systems
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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 ore 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 front rules, and other related issues are also discussed. Major approaches to extraction of logical rules based oil 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.
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