A study of visual behavior of multidimensional scaling for kernel perceptron algorithm
| Autor: | Kuo-Shong Wang, Shih-Hsing Chang, Che-Chang Hsu, Hung-Yuan Chung |
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| Rok vydání: | 2014 |
| Předmět: |
Kernel perceptron
business.industry Feature vector Hilbert space Multimodal distribution Pattern recognition Machine learning computer.software_genre Visualization symbols.namesake Artificial Intelligence symbols Multidimensional scaling Artificial intelligence Sources of error business Classifier (UML) computer Software Mathematics |
| Zdroj: | Neural Computing and Applications. 26:679-691 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-014-1746-2 |
| Popis: | The class imbalance problem occurs when the classifier is to detect a rare but important class. The purpose of this paper is to study whether possible sources of error are not only the imbalance but also other factors in combination, which lead to these misclassifications. The theoretical difficulties in purely predictive settings arise from the lack of visualization. Therefore, for kernel classifiers we propose the link with a kernel version of multidimensional scaling in high-dimensional feature space. The transformed version of the features specifically discloses the intrinsic structure of Hilbert space and is then used as inputs into a learning system: in the example, this prediction method is based on the SVMs-rebalance methodology. The graphical representations indicate the effects of masking, skewed, and multimodal distribution, which are also responsible for the poor performance. By studying the properties of the misclassifications, we can further develop ways to improve them. |
| Databáze: | OpenAIRE |
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