Floatcascade learning for fast imbalanced web mining
Proceedings of the 17th international conference on World Wide Web
Breast cancer survivability via AdaBoost algorithms
HDKM '08 Proceedings of the second Australasian workshop on Health data and knowledge management - Volume 80
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This paper presents a method of detecting faces based on cost-sensitive AdaBoost (CS-AdaBoost) algorithm. The two main differences between CS-AdaBoost algorithm and the naive AdaBoost are that (1) unequal initial weights are given to each training sample according to its misclassification cost, and (2) the weights are updated separately for positives and negatives at each boosting step. Due to these two variations, every stage of the face detector trained by CS-AdaBoost algorithm can more effectively focus on face samples than by the naive AdaBoost to achieve robust and high detection rate with modest false alarm rate, so that the final face detector can yield high detection rates, very low false positive rates, and robust performance. Experiments also demonstrate the effectiveness of our method.