Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/4862
Title: Symmetric Paralllel Class Expression Learning
Authors: Trần, Công Án
Marsland, Stephen
Dietrich, Jens
Guesgen, Hans W.
Keywords: Description logic learning
Parallel
Symmetric
Exception
Issue Date: 2017
Series/Report no.: Journal of Machine Learning Research;18 .- p.1-34
Abstract: In machine learning, one often encounters data sets where a general pattern is violated by a relatively small number of exceptions (for example, a rule that says that all birds can fly is violated by examples such as penguins). This complicates the concept learning process and may lead to the rejection of some simple and expressive rules that cover many cases. In this paper we present an approach to this problem in description logic learning by computing partial descriptions (which are not necessarily entirely complete) of both positive and negative examples and combining them. Our Symmetric Parallel Class Expression Learning approach enables the generation of general rules with exception patterns included. We demonstrate that this algorithm provides significantly better results (in terms of metrics such as accuracy, search space covered, and learning time) than standard approaches on some typical data sets. Further, the approach has the added benefit that it can be parallelised relatively simply, leading to much faster exploration of the search tree on modern computers.
URI: http://dspace.ctu.edu.vn/jspui/handle/123456789/4862
Appears in Collections:Tạp chí quốc tế

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