Abstract
New feature construction methods are presented. The methods are based on the idea that a smooth feature space facilitates inductive learning thus it is desirable for data mining The methods, Category-guided Adaptive Modeling (CAM) and Smoothness-driven Adaptive Modeling (SAM), are originally developed to model human perception of still images, where an image is perceived in a space of index colors. CAM is tested for a classification problem and SAM is tested for a Kansei scale value (the amount of the impression) prediction problem. Both algorithms have been proved to be useful as preprocess steps for inductive learning through the experiments. We also evaluate SAM using datasets from the UCI repository and the result has been promising.
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© 2002 Springer-Verlag Berlin Heidelberg
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Orihara, R., Murakami, T., Sueda, N., Sakurai, S. (2002). Information Space Optimization with Real-Coded Genetic Algorithm for Inductive Learning. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_30
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_30
Publisher Name: Physica, Heidelberg
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