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...

• Type: Conference
• Year: 2007
• Where: CIARP
• Authors: Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga
• Keywords: CIARP 2007, Individual Accuracy, Key Characteristic, Pattern Recognition, Subset Increases Diversity