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WIREs Data Mining Knowl Discov
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Fuzzy machine learning and data mininga

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Abstract The development of methods for machine learning and data mining has attracted increasing attention in the fuzzy set community in recent years. The aim of this paper is to assess the relevance of fuzzy set theory and fuzzy logic for these fields, highlighting potential contributions without concealing alleged limitations and shortcomings of current research. To this end, some typical applications of fuzzy logic will be reviewed, followed by a more systematic discussion of possible benefits of fuzzy methods. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 269–283 DOI: 10.1002/widm.34 This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning

Schematic presentation of the induction process and its ‘ingredients’: Model induction is realized by a learning algorithm that implements an induction principle (such as empirical or structural risk minimization) and operates on the set of properly preprocessed data. Additional background knowledge is incorporated in various ways, for example, through feature modeling or specification of the underlying model class (hypothesis space).

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Example of a fuzzy pattern tree representing a class whose instances have a high value for attribute A or satisfy a second criterion, namely, that B is low and that the average between the conditions that B and C are medium is high. The tree also shows the concrete propagation of values from the bottom to the top; in this case, the instance belongs to the class to the degree 0.7.

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Different shapes of a cell nucleus.

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