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Erschienen in: Soft Computing 1/2018

18.08.2016 | Methodologies and Application

Automatic support vector data description

verfasst von: Reza Sadeghi, Javad Hamidzadeh

Erschienen in: Soft Computing | Ausgabe 1/2018

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Abstract

Event handlers have wide range of applications such as medical assistant systems and fire suppression systems. These systems try to provide accurate responses based on the least information. Support vector data description (SVDD) is one of the appropriate tools for such detections, which should handle lack of information. Therefore, many efforts have been done to improve SVDD. Unfortunately, the existing descriptors suffer from weak data characteristic in sparse data sets and their tuning parameters are organized improperly. These issues cause reduction of accuracy in event handlers when they are faced with data shortage. Therefore, we propose automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm. To evaluate the performance of ASVDD, several experiments have been conducted on various data sets of UCI repository. The experimental results demonstrate superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC. In order to prove meaningful distinction between the accuracy results of the proposed method and the leading-edge ones, the Wilcoxon statistical test has been conducted.

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Metadaten
Titel
Automatic support vector data description
verfasst von
Reza Sadeghi
Javad Hamidzadeh
Publikationsdatum
18.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 1/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2317-5

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