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Bio-data Classification using Modified Additive Factor Model  

Cho, Min-Kook (경북대학교 전자전기컴퓨터학부)
Park, Hye-Young (경북대학교 전자전기컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.7, 2007 , pp. 667-680 More about this Journal
Abstract
The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.
Keywords
bio-data processing; pattern recognition; data generation model; additive factor model; similarity function; class factor; environment factor;
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