Scalable improved quick reduct: Sample based

dc.contributor.author	Sai Prasad, P. S.V.S.
dc.contributor.author	Raghavendra Rao, C.
dc.date.accessioned	2022-03-27T05:59:33Z
dc.date.available	2022-03-27T05:59:33Z
dc.date.issued	2012-08-20
dc.description.abstract	This paper develops an iterative sample based Improved Quick Reduct algorithm with Information Gain heuristic approach for recommending a quality reduct for large decision tables. The Methodology and its performance have been demonstrated by considering large datasets. It is recommended to use roughly 5 to 10% data size for obtaining an apt reduct. © 2012 Springer-Verlag.
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). v.7414 LNAI
dc.identifier.issn	03029743
dc.identifier.uri	10.1007/978-3-642-31900-6_5
dc.identifier.uri	http://link.springer.com/10.1007/978-3-642-31900-6_5
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/9036
dc.subject	ξ-approximate Reduct
dc.subject	IQuick Reduct
dc.subject	Quick Reduct
dc.subject	Variable Precision
dc.title	Scalable improved quick reduct: Sample based
dc.type	Book Series. Conference Paper
dspace.entity.type

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