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http://dx.doi.org/10.6109/jkiice.2012.16.1.033

Biological Early Warning Systems using UChoo Algorithm  

Lee, Jong-Chan (청운대학교 인터넷학과)
Lee, Won-Don (충남대학교 전기정보통신공학부 컴퓨터)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.16, no.1, 2012 , pp. 33-40 More about this Journal
Abstract
This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.
Keywords
BEWS; Fractal Dimension; Extended Data Expression; Weight; UChoo;
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