Titlebook: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques; Evangelos Triantaphyllou,Giovanni Felici Book 2006 Spri

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小步舞

Feature Selection for Data Mining, order to obtain a more essential and compact representation of the available information. The selected subset has to be small in size and must retain the information that is most useful for the specific application. The role of Feature Selection is particularly important when computationally expens

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Transformation of Rational Data and Set Data to Logic Data,ormula constructors, so that records can be processed that contain rational number and/or nominal values. A nominal value is an element or subset of a given finite set. In such cases, the rational numbers or nominal values must first be transformed to . values before the method may be applied. This

Cpap155

Data Farming: Concepts and Methods,data required for archival purposes. In some cases, the set of considered features might be large (a wide data set) and sufficient for extraction of knowledge. In other cases the data set might be narrow and insufficient to extract meaningful knowledge or the data may not even exist..Mining wide dat

甜瓜

Defiance

Multi-Attribute Decision Trees and Decision Rules,top-down induction of decision trees is one of the most popular techniques for inducing such classification models. Most of the research in decision tree induction has focused on single attribute trees, but in this chapter we review multi-attribute decision trees induction and discuss how such metho

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Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective,l organisms. This ability relies heavily on the quality of the raw information available about the target system. In reality, these raw information/data may contain uncertainty and fuzziness, that is, it may be imprecise or incomplete. A number of techniques, such as the Dempster-Shafer theory of be

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Cultivate

Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems,yze the effects of implicit fitness sharing, spatially distributed subpopulations, and combinations of the two, using a range of standard knowledge discovery tasks. The proposed models are compared based on (a) their ability to promote and/or maintain diversity across the evolving population; (b) th

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