منابع مشابه
Boosting Cost-Sensitive Trees
This paper explores two techniques for boosting cost-sensitive trees. The two techniques diier in whether the misclassiication cost information is utilized during training. We demonstrate that each of these techniques is good at diierent aspects of cost-sensitive classiications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induct...
متن کاملAdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical...
متن کاملBoosting Trees for Cost-Sensitive Classifications
This paper explores two boosting techniques for cost-sensitive tree classiications in the situation where misclassiication costs change very often. Ideally, one would like to have only one induction, and use the induced model for diierent misclassiication costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this ...
متن کاملParameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can b...
متن کاملCost-sensitive Boosting for Concept Drift
Concept drift is a phenomenon typically experienced when data distributions change continuously over a period of time. In this paper we propose a cost-sensitive boosting approach for learning under concept drift. The proposed methodology estimates relevance costs of ‘old’ data samples w.r.t. to ‘newer’ samples and integrates it into the boosting process. We experiment this methodology on usenet...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2011
ISSN: 0162-8828
DOI: 10.1109/tpami.2010.71