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

拖网

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

信任

恭维

Morsel

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

蚀刻术

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

确定的事

看法等

臭名昭著

序曲

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).