Enhanced active learning in developing highly interpretable decision support system

Mohd Salleh, Mohd Najib and Mohd Nawi, Nazri Enhanced active learning in developing highly interpretable decision support system. Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference .

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Developing highly interpretable commonly presents significant challenges to decision support system. In previous research work, partial information had provided poor result in the problem of learning classifiers. The behavior of some learning algorithm may only be explored by uncertainty analyses. We propose a novel information extraction by utilizing fuzzy measure in active learning to focus on the most informative instances. By integrating an expert knowledge as weight to the existing datasets, we overcome the uncertainty and appropriately assign partial datasets to the nearest clusters for classification. By choosing appropriate weights for pre labeled data, the nearest neighbor classifier consistently improves on the original classifier.

Item Type:Article
Uncontrolled Keywords:decision support system ; fuzzy cluster analysis ; uncertainty
Subjects:T Technology > T58.5-58.64 Information technology
Divisions:Faculty of Science Computer and Information Technology > Department of Software Engineering
ID Code:3237
Deposited By:Norfauzan Md Sarwin
Deposited On:08 Nov 2012 09:34
Last Modified:08 Nov 2012 09:34

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