Expert Systems with Applications: An International Journal
Reducing the run-time complexity of support vector data descriptions
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Two-class support vector data description
Pattern Recognition
Expert Systems with Applications: An International Journal
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Supervised learning approaches and feature selection - a case study in diabetes
International Journal of Data Analysis Techniques and Strategies
Parallel support vector data description
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Density weighted support vector data description
Expert Systems with Applications: An International Journal
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The purpose of data description is to give a compact description of the target data that represents most of its characteristics. In a support vector data description (SVDD), the compact description of target data is given in a hyperspherical model, which is determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD, however, it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data. To address the issue in SVDD methodology, we propose a new SVDD by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set. Moreover, for a real application, we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem. Experiments with various real data sets show promising results