Abstract
This paper presents an investigation into the classification of a difficult data set containing large intra-class variability but low interclass variability. Standard classifiers are weak and fail to achieve satisfactory results however, it is proposed that a combination of such weak classifiers can improve overall performance. The paper also introduces a novel evolutionary approach to fuzzy rule generation for classification problems.
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Rosin, P.L., Nyongesa, H.O. (2000). Combining Evolutionary, Connectionist, and Fuzzy Classification Algorithms for Shape Analysis. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_9
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DOI: https://doi.org/10.1007/3-540-45561-2_9
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