Download PDF by Richard Jensen: Computational Intelligence and Feature Selection: Rough and

By Richard Jensen

ISBN-10: 0470229756

ISBN-13: 9780470229750

The tough and fuzzy set techniques awarded right here open up many new frontiers for endured study and improvement. Computational Intelligence and have choice offers readers with the heritage and basic rules in the back of characteristic choice (FS), with an emphasis on thoughts according to tough and fuzzy units. For readers who're much less conversant in the topic, the ebook starts with an creation to fuzzy set idea and fuzzy-rough set concept.

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Extra resources for Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

Example text

Even in some 16 SET THEORY cases where precise knowledge is available, fuzziness may be a concomitant of complexity involved in the reasoning process. The adoption of fuzzy sets helps to ease the requirement for encoding uncertain domain knowledge. For example, labels like small, medium, and large have an intuitive appeal to represent values of some physical attributes. However, if they are defined with a semantic interpretation in terms of crisp intervals, such as small = {x|x > 0, x 1} medium = {x|x > 0, x ≈ 1} the representation may lead to rather nonintuitive results.

The clustering method itself can be thought of as the process of organizing objects into groups whose members share similar CRISP APPROACHES 43 properties. A cluster, then, is a collection of objects that are similar but are dissimilar to objects belonging to other clusters. 3 where the concept of equivalence classes is similar to the clustering concept. Objects belong to an equivalence class if they contain the same attribute values for a given set of attributes. Each class can be thought of as a cluster, with the similarity criterion being the strict requirement that objects are identical.

Indeed, since its invention, rough set theory has been successfully utilized to devise mathematically sound and often computationally efficient techniques for addressing problems such as hidden pattern discovery from data, data reduction, data significance evaluation, decision rule generation, and data-driven inference interpretation [263]. Owing to the recognition of the existing and potential important impact of this theory, it has attracted worldwide attention of further research and development, resulting in various extensions to the original theory and increasingly widening fields of application.

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Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches by Richard Jensen

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