In recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers.

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Abstract. Most descriptor-based keypoint recognition methods require computationally expensive patch preprocessing to obtain insensitivity to various kinds of deformations. This limits their applicability towards realtime applications on low-powered devices such as mobile phones. In this paper, we focus on descriptors which are relatively weak (i.e. sensitive to scale and rotation), and present a classification-based approach to improve their robustness and efficiency to achieve real-time matching. We demonstrate our method by applying it to BRIEF [7]

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