A Coherent Framework For Selection And Optimized Weighted Average Aggregation Of Ensemble Of Heterogeneous Classifiers Using Global Optimization Approach To Enhance Prediction Performance In Learning Environment

dc.contributor.guide	N Rama
dc.coverage.spatial
dc.creator.researcher	Princy Christy A
dc.date.accessioned	2022-03-04T10:49:40Z
dc.date.available	2022-03-04T10:49:40Z
dc.date.awarded
dc.date.completed	2021
dc.date.registered
dc.description.abstract	newlineA Coherent Framework For Selection And Optimized Weighted Average Aggregation Of Ensemble Of Heterogeneous Classifiers Using Global Optimization Approach To Enhance Prediction Performance In Learning Environment
dc.description.note
dc.format.accompanyingmaterial	DVD
dc.format.dimensions
dc.format.extent
dc.identifier.uri	http://hdl.handle.net/10603/366729
dc.language	English
dc.publisher.institution	Department of Computer Science
dc.publisher.place	Chennai
dc.publisher.university	University of Madras
dc.relation
dc.rights	university
dc.source.university	University
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	Computer Science
dc.subject.keyword	Computer Science Information Systems
dc.title	A Coherent Framework For Selection And Optimized Weighted Average Aggregation Of Ensemble Of Heterogeneous Classifiers Using Global Optimization Approach To Enhance Prediction Performance In Learning Environment
dc.title.alternative
dc.type.degree	Ph.D.