Computational intelligence methods for rule-based data understanding

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 foc...

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Bibliographic Details
Published in:Proceedings of the IEEE 2004-05, Vol.92 (5), p.771-805
Main Authors: Duch, W., Setiono, R., Zurada, J.M.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computational intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Data mining
Data processing. List processing. Character string processing
Data visualization
Decision trees
Exact sciences and technology
Extraction
Intelligence
Machine learning
Memory organisation. Data processing
Neural networks
Optimization
Probability
Prototypes
Rule based
Similarity
Software
Stability
Statistical analysis
Statistical methods
Studies
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Summary: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.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2004.826605