Abstract
Instances selection is an important task in the data preparation phase of knowledge discovery and data mining (KDD). Instances selection techniques are largely studied especially in the classification problem. However, little work has been done to implement instances selection in fuzzy modeling application. In this paper, we present a framework for fuzzy modeling using the granular instances selection. This method is based on the information granulation approach to select the best subset of instances for constructing the fuzzy model. We show that by using Particle Swarm Optimization (PSO) for searching the best level of granularity for each feature can improve the predictive accuracy of the fuzzy model.
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Ahmad, S.S.S., Pedrycz, W. (2011). Granular Instances Selection for Fuzzy Modeling. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_8
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DOI: https://doi.org/10.1007/978-3-642-23199-5_8
Publisher Name: Springer, Berlin, Heidelberg
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