Incremental learning in AttributeNets with dynamic reduct and IQuickReduct

Authors:
P. S. V. S. Sai Prasad;K. Hima Bindu;C. Raghavendra Rao
Affiliations:
Department of Computer and Information Sciences,University of Hyderabad, Hyderabad, Andhra Pradesh, India;Department of Computer and Information Sciences,University of Hyderabad, Hyderabad, Andhra Pradesh, India;Department of Computer and Information Sciences,University of Hyderabad, Hyderabad, Andhra Pradesh, India
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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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.