About: A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90295 A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification
sameAs	A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification
http://eprints.org/ontology/hasDocument	A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (PDF) A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (Other) A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (Other) A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (Other) A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (Other) A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (Other)
http://eprints.org/ontology/hasPublished	A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (PDF)
dc:hasVersion	A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification (PDF)
Title	A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-09-14
Creator	Fernando E.B. Otero Helen C. S. C. Lima Luiz H.C. Merschmann Marcone J. F. Souza
status	peer reviewed published
Publisher	IEEE
abstract	Feature selection is a widespread preprocessing step in the data mining field. One of its purposes is to reduce the number of original dataset features to improve a predictive model’s performance. Despite the benefits of feature selection for the classification task, to the best of our knowledge, few studies in the literature address feature selection for the hierarchical classification context. This paper proposes a novel feature selection method based on the general variable neighborhood search metaheuristic, combining a filter and a wrapper step, wherein a global model hierarchical classifier evaluates feature subsets. We used twelve datasets from the proteins and images domains to perform computational experiments to validate the effect of the proposed algorithm on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method for feature selection led to predictive performances that were consistently better than or equivalent to that obtained by using all features with the benefit of reducing the number of features needed, which justifies its efficiency for the hierarchical classification scenario.
Is Part Of	IEEE Access https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/90295#authors
volume	9
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90295 A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

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