@inproceedings{38449085234,
    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 trade off between both components of the individual accuracy using a parameter λ ∈ [0, 1] that changes the cost of a misclassification on the second subset. Experiments are provided using well-known classification problems obtained from UCI. Results are also compared with boosting and bagging. © Springer-Verlag Berlin Heidelberg 2007.",
    year = "2007",
    title = "Bagging with asymmetric costs for misclassified and correctly classified examples",
    volume = "4756 LNCS",
    pages = "694-703",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
}

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