Abstract
Many real-world classification algorithms can not be applied unless the continuous attributes are discretized and the interval discretization methods are used in many machine learning techniques. It is hard to determine the intervals for the discretization of numerical attributes that has an infinite number of candidates. And interval discretization methods are based on a crisp set, a value in a continuous attribute must belong to only one interval. They are often not proper for describing a value located around the boundaries of intervals. Fuzzy partioning is an attractive method for those cases in classification problems. An important decision in fuzzy partitioning is about the positions of interval boundaries and the degrees of overlapping in the fuzzy sets. We optimize the parameters that specify fuzzy partitioning by genetic algorithms.
This work was supported by Brain Korea 21 Project. The ICT at Seoul National University provided research facilities for this study.
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© 2004 Springer-Verlag Berlin Heidelberg
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Choi, YS., Moon, BR. (2004). Genetic Fuzzy Discretization for Classification Problems. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_138
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DOI: https://doi.org/10.1007/978-3-540-24855-2_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
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