Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis
IEEE Intelligent Systems
Prediction of MHC II-binding peptides using rough set-based rule sets ensemble
Applied Intelligence
Dynamic EMCUD for knowledge acquisition
Expert Systems with Applications: An International Journal
MRM: A matrix representation and mapping approach for knowledge acquisition
Knowledge-Based Systems
Rule induction for prediction of MHC II-binding peptides
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A rule sets ensemble for predicting MHC II-Binding peptides
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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The attribute reduction and rule generation (the attribute value reduction) are two main processes for knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition aimed at the above processes was proposed. In the attribute reduction process, the conventional logic computation was transferred to a matrix computation along with some added thoughts on the evolution computation used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs were presented in detail. In the rule generation process, the orderly attribute value reduction algorithm (OAVRA), which simplified the complexity of rule knowledge, was presented. The approach provided an effective and efficient method for knowledge acquisition that was supported by the experimentation.