Li, Yuhua and Maguire, LP (2010) Selecting Critical Patterns Based on Local Geometrical and Statistical Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 pp, DOI: 10.1109/TPAMI.2010.18 [Internet publication]
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Pattern selection methods have been traditionally developed with a dependency on a specific classifier. In contrast this paper presents a method that selects critical patterns deemed to carry essential information applicable to train those types of classifiers which require spatial information of the training dataset. Critical patterns include those edge patterns that define the boundary and those border patterns that separate classes. The proposed method selects patterns from a new perspective, primarily based on their location in input space. It determines class edge patterns with the assistance of approximated tangent hyperplane of a class surface. It also identifies border patterns between classes using local probability. The proposed method is evaluated on benchmark problems using popular classifiers including multilayer perceptrons, radial basis functions, support vector machines and nearest neighbors. The proposed approach is also compared with four state-of-the-art approaches and it is shown to provide similar but more consistent accuracy from a reduced data set. Experimental results demonstrate that it selects patterns sufficient to represent class boundary and to preserve the decision surface.
|Item Type:||Internet publication|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Intelligent Systems Research Centre
|Deposited By:||Professor Liam Maguire|
|Deposited On:||02 Mar 2011 14:28|
|Last Modified:||15 Jun 2011 11:09|
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