About: A Framework for Feature Selection to Exploit Feature Group Structures

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Framework for Feature Selection to Exploit Feature Group Structures
Creator	Chan, Jeffrey Karunasekera, Shanika Perera, Kushani
Source	PMC
abstract	Filter feature selection methods play an important role in machine learning tasks when low computational costs, classifier independence or simplicity is important. Existing filter methods predominantly focus only on the input data and do not take advantage of the external sources of correlations within feature groups to improve the classification accuracy. We propose a framework which facilitates supervised filter feature selection methods to exploit feature group information from external sources of knowledge and use this framework to incorporate feature group information into minimum Redundancy Maximum Relevance (mRMR) algorithm, resulting in GroupMRMR algorithm. We show that GroupMRMR achieves high accuracy gains over mRMR (up to [Formula: see text]35%) and other popular filter methods (up to [Formula: see text]50%). GroupMRMR has same computational complexity as that of mRMR, therefore, does not incur additional computational costs. Proposed method has many real world applications, particularly the ones that use genomic, text and image data whose features demonstrate strong group structures.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_61
has license	no-cc
sha1sum (hex)	15ed249c362f740038f392127be247ec58b0eac1
schema:url	https://doi.org/10.1007/978-3-030-47426-3_61
resource representing a document's title	A Framework for Feature Selection to Exploit Feature Group Structures
has PubMed Central identifier	PMC7206161
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:15ed249c362f740038f392127be247ec58b0eac1#body_text
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