Titlebook: Hierarchical Feature Selection for Knowledge Discovery; Application of Data Cen Wan Book 2019 Springer Nature Switzerland AG 2019 Bioinfor

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Feature Selection Paradigms, performance of classifiers. The dataset with the full set of features is input to the feature selection method, which will select a subset of features to be used for building the classifier. Then the built classifier will be evaluated, by measuring its predictive accuracy. Irrelevant features can b

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Eager Hierarchical Feature Selection,dings of the international joint conference on natural language processing, Nagoya, Japan, 2013, [.]), Bottom-up Hill Climbing Feature Selection (HC) (Wang et al, Proceedings of the 26th Australasian computer science conference, Darlinghurst, Australia, 2003, [.]), Greedy Top-down Feature Selection

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Dorsal-Kyphosis

Conclusions and Research Directions,e of different classifiers. Their better performance also proves that exploiting the hierarchical dependancy information as a type of searching constraint usually leads to a feature subset containing higher predictive power. However, note that, those hierarchical feature selection methods still have

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Lazy Hierarchical Feature Selection,rmatics and biomedicine (BIBM 2013), Shanghai, China, pp 373–380, [.], Wan et al., IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275, [.]). Those three hierarchical feature selection methods are categorised as filter methods (discussed in Chap.?., i.e. feature selection is conducted before the learning process of classifier).