Trinity College Dublin, Department of Computer Science
Cunningham, Pádraig; Zenobi, Gabriele. 'Case Representation Issues for Case-Based Reasoning from Ensemble Research'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2001-10, 2001, pp12
Computer Science Technical Report TCD-CS-2001-10
Ensembles of classifiers will produce lower errors than the member
classifiers if there is diversity in the ensemble. One means of producing this
diversity in nearest neighbour classifiers is to base the member classifiers on
different feature subsets. In this paper we show four examples where this is the
case. This has implications for the practice of feature subset selection (an
important issue in CBR and data-mining) because it shows that there is no best
feature subset to represent a problem. We show that if diversity is emphasised
in the development of the ensemble that the ensemble members appear to be
local learners specializing in sub-domains of the problem space. The paper
concludes with some proposals on how analysis of ensembles of local learners
might provide insight on problem-space decomposition for hierarchical CBR.
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