A Novel Biologically and Psychologically Inspired Fuzzy Decision Support System: Hierarchical Complementary Learning

A computational intelligent system that models the human cognitive abilities may promise significant performance in problem learning because a human is effective in learning and problem solving. Functionally modeling the human cognitive abilities not only avoids the details of the underlying neural...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2008-01, Vol.5 (1), p.67-79
Main Authors: Tan, Tuan Zea, Ng, Geok See, Quek, Chai
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biological system modeling
Biology computing
Cognition & reasoning
cognitive learning
Competitive intelligence
complementary learning
Computational intelligence
decision support
Decision support systems
Decision Support Techniques
Decision Theory
Fuzzy Logic
fuzzy neural network
Fuzzy systems
hierarchical model
Humans
Intelligent systems
Neoplasms - diagnosis
Pattern recognition
Pattern Recognition, Automated - methods
Psychology
Reproducibility of Results
Sensitivity and Specificity
Studies
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Summary:A computational intelligent system that models the human cognitive abilities may promise significant performance in problem learning because a human is effective in learning and problem solving. Functionally modeling the human cognitive abilities not only avoids the details of the underlying neural mechanisms performing the tasks but also reduces the complexity of the system. The complementary learning mechanism is responsible for human pattern recognition, that is, a human attends to positive and negative samples when making a decision. Furthermore, human concept learning is organized in a hierarchical fashion. Such hierarchical organization allows the divide-and-conquer approach to the problem. Thus, integrating the functional models of hierarchical organization and complementary learning can potentially improve the performance in pattern recognition. Hierarchical complementary learning (HCL) exhibits many of the desirable features of pattern recognition. It is further supported by the experimental results that verify the rationale of the integration and that the HCL system is a promising pattern recognition tool.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2007.1064