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An algorithm which learns multiple covers via integer linear programming. Part II: experimental results and conclusions

An algorithm which learns multiple covers via integer linear programming. Part II: experimental... Presents an inductive machine learning algorithm, CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. The complexity of the algorithm is analysed and the results are compared with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. Published in two parts: I – The CLILP2 algorithm; II – Experimental results and conclusions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

An algorithm which learns multiple covers via integer linear programming. Part II: experimental results and conclusions

Kybernetes , Volume 24 (3): 13 – Apr 1, 1995

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Publisher
Emerald Publishing
Copyright
Copyright © 1995 MCB UP Ltd. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684929510147029
Publisher site
See Article on Publisher Site

Abstract

Presents an inductive machine learning algorithm, CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. The complexity of the algorithm is analysed and the results are compared with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. Published in two parts: I – The CLILP2 algorithm; II – Experimental results and conclusions.

Journal

KybernetesEmerald Publishing

Published: Apr 1, 1995

Keywords: Algorithms; Computer programming; Problem solving

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