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CIARP
2007
Springer

Bagging with Asymmetric Costs for Misclassified and Correctly Classified Examples

9 years 4 months ago
Bagging with Asymmetric Costs for Misclassified and Correctly Classified Examples
Abstract. Diversity is a key characteristic to obtain advantages of combining predictors. In this paper, we propose a modification of bagging to explicitly trade off diversity and individual accuracy. The procedure consists in dividing the bootstrap replicates obtained at each iteration of the algorithm in two subsets: one consisting of the examples misclassified by the ensemble obtained at the previous iteration, and the other consisting of the examples correctly recognized. A high individual accuracy of a new classifier on the first subset increases diversity, measured as the value of the Q statistic between the new classifier and the existing classifier ensemble. A high accuracy on the second subset on the other hand, decreases diversity. We tradeoff between both components of the individual accuracy using a parameter [0, 1] that changes the cost of a misclassification on the second subset. Experiments are provided using well-known classification problems obtained from UCI. Result...
Ricardo Ñanculef, Carlos Valle, Héct
Added 13 Aug 2010
Updated 13 Aug 2010
Type Conference
Year 2007
Where CIARP
Authors Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga
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