Incremental learning in attributenets with dynamic reduct and IQuickReduct

dc.contributor.author	Sai Prasad, P. S.V.S.
dc.contributor.author	Hima Bindu, K.
dc.contributor.author	Raghavendra Rao, C.
dc.date.accessioned	2022-03-27T05:59:52Z
dc.date.available	2022-03-27T05:59:52Z
dc.date.issued	2011-10-19
dc.description.abstract	Incremental learning is becoming more essential in the real world problems in which a decision system is being updated frequently. AttributeNets is a classifier whose representation allows updating the classifier when new data is added incrementally. In this paper the impact of reduct on the performance of AttributeNets as an Incremental Classifier is investigated. This philosophy has been demonstrated by adopting two varieties of reducts, namely dynamic reduct and IQuickReduct. These reducts were used to study the capability of AttributeNets for classification with reduced attributes. © 2011 Springer-Verlag.
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). v.6954 LNAI
dc.identifier.issn	03029743
dc.identifier.uri	10.1007/978-3-642-24425-4_27
dc.identifier.uri	http://link.springer.com/10.1007/978-3-642-24425-4_27
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/9053
dc.subject	AttributeNets
dc.subject	Dynamic Reduct
dc.subject	IQuickReduct
dc.subject	RSES
dc.title	Incremental learning in attributenets with dynamic reduct and IQuickReduct
dc.type	Book Series. Conference Paper
dspace.entity.type

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