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Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification”. In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of the cost-sensitive measures made by the proposed algorithms, and extensively examine their empirical performance on a variety of cost-sensitive online classification tasks. Finally, we demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be a highly efficient and effective tool to tackle cost-sensitive online classification tasks in various application domains. [ABSTRACT FROM AUTHOR]

Real-world data is noisy and can often suffer from corruptions or incomplete values that may impact the models created from the data. To build accurate predictive models, data acquisition is usually adopted to prepare the data and complete missing values. However, due to the significant cost of doing so and the inherent correlations in the data set, acquiring correct information for all instances is prohibitive and unnecessary. An interesting and important problem that arises here is to select what kinds of instances to complete so the model built from the processed data can receive the "maximum" performance improvement. This problem is complicated by the reality that the costs associated with the attributes are different, and fixing the missing values of some attributes is inherently more expensive than others. Therefore, the problem becomes that given a fixed budget, what kinds of instances should be selected for preparation, so that the learner built from the processed data set can maximize its performance? In this paper, we propose a solution for this problem, and the essential idea is to combine attribute costs and the relevance of each attribute to the target concept, so that the data acquisition can pay more attention to those attributes that are cheap in price but informative for classification. To this end, we will first introduce a unique Economical Factor (EF) that seamlessly integrates the cost and the importance (in terms of classification) of each attribute. Then, we will propose a cost-constrained data acquisition model, where active learning, missing value prediction, and impact-sensitive instance ranking are combined for effective data acquisition. Experimental results and comparative studies from real-world data sets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]