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Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called...
As image spam becomes widespread and does a lot of harm, it is more important to filter such spam effectively for now. In this paper, We propose a feature extraction scheme that focus on low-level features (metadata and visual features) of image, which can making classification rapid. They are effective because of not rely on extracting text and analyzing the content of email. a one-class SVM classifier...
This paper considers the low-level feature modeling problem in image spam classification, in which most of the prevalent content based spam filters are shown to be inefficient because their OCR procedure are vulnerable to text obscuring attacks from spammers. We first built up a basic feature set through a low-level feature extraction process, and then proposed a stepwise regression method to determine...
In this paper, a new method is proposed for discriminating spam images from non-spam images. This method extracts edge features of a binarized image by using higher-order local autocorrelation(HLAC), and then input those features to support vector machine (SVM) for classification. Our method has three unique characteristics. First, the method extracts edge features which can represent major edge properties...
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