Inferring decision trees using the minimum description length principle
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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Behavioral sequences of the medaka (Oryzias latipes) were continuously investigated through an automatic image recognition system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon (0.1 mg/l) during a 1 hour period. The observation of behavior through the movement tracking program showed many patterns of the medaka. After much observation, behavioral patterns were divided into four basic patterns: active-smooth, active-shaking, inactive-smooth, and inactive-shaking. The “smooth” and “shaking” patterns were shown as normal movement behavior. However, the “shaking” pattern was more frequently observed than the “smooth” pattern in medaka specimens that were treated with insecticide. Each pattern was classified using a devised decision tree after the feature choice. It provides a natural way to incorporate prior knowledge from human experts in fish behavior and contains the information in a logical expression tree. The main focus of this study was to determine whether the decision tree could be useful for interpreting and classifying behavior patterns of the medaka.