About: On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification
Creator	Fister, Dušan Fister, Iztok Podgorelec, Vili Vrbančič, Grega
Source	PMC
abstract	With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50426-7_2
has license	no-cc
sha1sum (hex)	c2ecbcc6993cef5532ec055b90b525ab5b8d141a
schema:url	https://doi.org/10.1007/978-3-030-50426-7_2
resource representing a document's title	On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification
has PubMed Central identifier	PMC7302567
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:c2ecbcc6993cef5532ec055b90b525ab5b8d141a#body_text
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