Junxin Zhang

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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(More)
In this paper, we present an overview of our research in real time data mining-based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are(More)
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning where new data become available periodically. We propose two new ways to apply AdaBoost. The first allows the use of a small sample of the weighted training set to compute a weak(More)
Traditional methods for sex identification are not applicable to sexually monomorphic species, leading to difficulties in the management of their breeding programs. To identify sex in sexually monomorphic birds, molecular methods have been established. Two established primer pairs (2550F/2718R and p8/p2) amplify the CHD1 gene region from both the Z and W(More)
Classification is a data mining technique used to predict group membership for data instances. Cost-sensitive classifier is relatively new field of research in the data mining and machine learning communities. They are basically used for classification tasks under the cost-based model, unlike the error-based model. Error based classifier AdaBoost is a(More)
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