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This chapter focuses on those recent techniques for feature selection that mploy a rough set-based methodology for this purpose, highlighting current trends in this promising area. Rough set fundamentals are introduced with a simple example to illustrate its operation. Several extensions to this theory are. In Visual Studio .NET Using Barcode creator for . NET framework Control to generate, create Data Matrix CC200 image in Visual Studio .NET applications.Related: EAN-13 Generator .NET , EAN 128 Generation .NET , .NET UPC-A Generation





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Aug 4, 2016 · Tesseract is a well-known open source OCR library that can be integrated with Android apps. It was originally developed by Hewlett Packard ...

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Using Barcode creator for VS .NET Control to generate, create Data Matrix d barcode image in .NET framework applications. Scanner In Visual Studio .NET Using Barcode recognizer for . Feature selection using rough sets theory In Proceedings of 11th International Conference on Machine Learning. Morgan Kaufmann, pp. 213 226. 1993. 237. N. N. Morsi and M. M. Yakout. Axiomatics for fuzzy rough sets. Fuzzy Sets Sys. 100(1 3): 327 342. 1998. 238. A. Moukas and P. Maes. Amalthaea: An evolving multi-agent information ltering and discovery system for the WWW. J. Autonomous Agents Multi-Agent Sys. 1(1): 59 88. 1998. 239. I. Moulinier. A framework for comparing text categorization approaches. In Proceedings of AAAI Spring Symposium on Machine Learning in Information Access. Washington: AAAI, pp. 61 68. 1996. 240. S. Muggleton. Inverse entailment and Progol. New Gen. Comput. 13(3 4): 245 286. 1995. 241. S. Muggleton and L. De Raedt. Inductive logic programming. J. Logic Program. 19/20: 629 679. 1994. 242. S. Muggleton and C. Feng. Ef cient induction of logic programs. In Proceedings of 1st Conference on Algorithmic Learning Theory. Springer/Ohmsha pp. 368 381. 1990. 243. T. Nakashima, H. Ishibuchi, and T. Murata. Evolutionary algorithms for constructing linguistic rule-based systems for high-dimensional pattern classi cation problems. In Proceedings of 1998 IEEE International Conference on Evolutionary Computation. Piscataway, NJ: IEEE Press, pp. 752 757. 1998. 244. S. Nanda and S. Majumdar. Fuzzy rough sets. Fuzzy Sets Sys. 45: 157 160, 1992. 245. D. Nauck, F. Klawonn, R. Kruse, and F. Klawonn. Foundations of Neuro-Fuzzy Systems. New York: Wiley. 1997.Related: 

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Using Barcode Control SDK for VS NET Control to generate, create, read, scan barcode image in NET framework applications The task of feature selection is to select a subset of the riginal features present in a given dataset that provides most of the useful information Hence, after selection has taken place, the dataset should still have most of the important information still present In fact, good FS techniques should be able to detect and ignore noisy and misleading features The result of this is that the dataset quality might even increase after selection There are two feature qualities that must be considered by FS methods: relevancy and redundancy A feature is said to be relevant if it is predictive of the decision feature(s); otherwise, it is irrelevant A feature is considered to be redundant if it is highly correlated with other features.

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An informative feature is one that is highly correlated with the decision concept(s) but is highly uncorrelated with other features (although low correlation does not mean absence of relationship) Similarly subsets of features should exhibit these properties of relevancy and nonredundancy if they are to be useful In [171] two notions of feature relevance, strong and weak relevance, were de ned If a feature is strongly relevant, this implies that it cannot be removed from the dataset without resulting in a loss of predictive accuracy If it is weakly relevant, then the feature may sometimes contribute to accuracy, though this depends on which other features are considered These de nitions are independent of the speci c learning algorithm used However, this no guarantee that a relevant feature will be useful to such an algorithm.

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It is quite possible for two features to be useless individually, and yet highly predictive if taken together In FS terminology, they may be both redundant and irrelevant on their own, but their combination provides invaluable information For example, in the exclusive-or problem, where the classes are not linearly separable, the two features on their own provide no information concerning this separability It is also the case that they are uncorrelated with each other However, when taken together, the two features are highly informative and can provideRelated: .

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