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Uncertainty Modeling for Data Mining: A Label Semantics by Zengchang Qin, Yongchuan Tang

By Zengchang Qin, Yongchuan Tang

Machine studying and knowledge mining are inseparably attached with uncertainty. The observable info for studying is generally vague, incomplete or noisy. Uncertainty Modeling for information Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based concept for modeling uncertainty. a number of new info mining algorithms in response to label semantics are proposed and validated on real-world datasets. A prototype interpretation of label semantics and new prototype-based information mining algorithms also are mentioned. This e-book deals a helpful source for postgraduates, researchers and different execs within the fields of knowledge mining, fuzzy computing and uncertainty reasoning.

Zengchang Qin is an affiliate professor on the university of Automation technological know-how and electric Engineering, Beihang collage, China; Yongchuan Tang is an affiliate professor on the collage of machine technological know-how, Zhejiang collage, China.

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Instead, we will focus on the modelling part of data mining with the aim of providing effective and interpretable algorithms. There are a number of different approaches toward data mining. 3 Data Mining and Algorithms 25 perspective [38] . Each perspective lays strong emphasis on different aspects of data mining. (1) Efficiency of data mining: this is emphasized by the database perspective. A database often has a huge amount of data and for some reasons of computational complexity some algorithms simply cannot be applied to such a large data set.

6 An illustration of a two-layer neural network. The arrows represent connections with weights wi j attached to them The most commonly used algorithms for multi-layer ANN are the BackPropagation (BP) neural networks [44] . In this book, BP-NN will be used for later comparison studies. Learning by BP algorithm for NN proceeds as follows: example inputs are presented to the network and, if the network computes an output vector that matches the target, nothing is done. If there is an error, then the weights are adjusted to reduce this error.

Naive Bayes). The former are referred to as discrete classifiers and the latter as probabilistic classifiers or rankers, because the membership probabilities can be used to rank instances from most to least likely positive. By setting a threshold, a rankers can act as a discrete classifier. The area under the curve (AUC) of ROC is used to measure the quality of ranking for a probabilistic classifier [28,53] . Ling et al. proved that AUC is statistically consistent and more discriminating than the accuracy measure [54] .

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