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Learning accurate and understandable...

Chen, Fei.

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Learning accurate and understandable rules from SVM classifiers.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning accurate and understandable rules from SVM classifiers./
作者:	Chen, Fei.
面頁冊數:	58 p.
附註:	Source: Masters Abstracts International, Volume: 44-01, page: 0385.
Contained By:	Masters Abstracts International44-01.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR03357
ISBN:	0494033576

Learning accurate and understandable rules from SVM classifiers.
Chen, Fei.

Learning accurate and understandable rules from SVM classifiers. - 58 p.

Source: Masters Abstracts International, Volume: 44-01, page: 0385.

Thesis (M.Sc.)--Simon Fraser University (Canada), 2004.

Despite of their impressive classification accuracy in many high dimensional applications, Support Vector Machine (SVM) classifiers are hard to understand because the definition of the separating hyperplanes typically involves a large percentage of all features. In this paper, we address the problem of understanding SVM classifiers, which has not yet been well-studied. We formulate the problem as learning models to approximate trained SVMs that are more understandable while preserve most of the SVM's classification quality. Our method learns a set of If-Then rules that are generally considered to be understandable and that allow an explicit control of their complexity to meet user-supplied requirements. The adoption of the unordered rule learning paradigm, along with exploiting the trained SVMs helps overcome the weakness of standard rule learners in high dimensional feature spaces. A pruning method is employed to maximize the accuracy of the resulting rule set for some user-specified complexity. Experiments demonstrate that the accuracy of the rule set is close to that achieved by SVMs and keeps stable even with substantial decreases of the rule complexity.

ISBN: 0494033576Subjects--Topical Terms:

626642
Computer Science.

Learning accurate and understandable rules from SVM classifiers.
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520 $a Despite of their impressive classification accuracy in many high dimensional applications, Support Vector Machine (SVM) classifiers are hard to understand because the definition of the separating hyperplanes typically involves a large percentage of all features. In this paper, we address the problem of understanding SVM classifiers, which has not yet been well-studied. We formulate the problem as learning models to approximate trained SVMs that are more understandable while preserve most of the SVM's classification quality. Our method learns a set of If-Then rules that are generally considered to be understandable and that allow an explicit control of their complexity to meet user-supplied requirements. The adoption of the unordered rule learning paradigm, along with exploiting the trained SVMs helps overcome the weakness of standard rule learners in high dimensional feature spaces. A pruning method is employed to maximize the accuracy of the resulting rule set for some user-specified complexity. Experiments demonstrate that the accuracy of the rule set is close to that achieved by SVMs and keeps stable even with substantial decreases of the rule complexity.
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