Abstract
Constructive Induction is a preprocessing step applied to representation space prior to machine learning algorithms and transforms the original representation with complex interaction into a representation that highlights regularities and is easy to be learned. In this paper a Fuzzy-GA wrapper-based constructive induction system is represented. In this model an understandable real-coded GA is employed to construct new features and a fuzzy system is designed to evaluate new constructed features and select more relevant features. This model is applied on a PNN classifier as a learning algorithm and results show that integrating PNN classifier with Fuzzy-GA wrapper-based constructive induction module will improve the effectiveness of the classifier.
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HajAbedi, Z., Kangavari, M.R. (2009). A Fuzzy-GA Wrapper-Based Constructive Induction Model. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_47
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DOI: https://doi.org/10.1007/978-3-642-04020-7_47
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