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Traditional classification algorithms are difficult in dealing with imbalance data. This paper proposes a classification algorithm called CascadeBoost, which combines with the advantages of boosting algorithm and cascade model that can learn imbalance data. Cascade model allows the pre-training data to be balanced by gradually reducing the number of the major class; and then the most rich information...
Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm - Boost-SVM, which puts SVM into AdaBoost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples...
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