Abstract
In this talk we deal with the problem of dimensionality reduction in a fuzzy rule-based framework. We consider dimensionality reduction through feature extraction as well as through feature selection. For the former approach, we use Sammon’s stress function as a criterion for structure-preserving dimensionality reduction. For feature selection we propose an integrated framework, which embeds the feature selection task into the classifier design task. This method uses a novel concept of feature modulating gate and it can exploit the subtle nonlinear interaction between the tool (here a fuzzy rule based system), the features and the task at hand. This method is then extended to Takagi-Sugeno (TS) model for function approximation/prediction problem. The effectiveness of these methods is demonstrated using several data sets.
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Pal, N.R. (2012). Fuzzy Rule-Based Approaches to Dimensionality Reduction. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_3
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DOI: https://doi.org/10.1007/978-3-642-27387-2_3
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