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This paper studies the imbalanced data classifycation problem and proposes bi-directional sampling based on clustering (BDSK) for the imbalanced data classification. This algorithm combines SMOTE over-sampling algorithm and under-sampling algorithm based on K-Means to solve the within-class imbalance problem and the between-class imbalance problem. It not only avoid induce too much noise but also...
Text classification is one of the practices of knowledge discovery. Designation of the classifier is the most important par of text classification. Comparing with the methods based on statistic theory, classification based on rule learning is a better one on some situations. A granular computing approach is proposed to learn rules by constructing a granule network while classifying texts. The algorithm...
Style-based text authorship identification extracts features from authorship-known texts, constructs classifier and then identifies disputed texts. Authorship identification belongs to the domain of style classification and is a branch of text classification. In contrast with text classification which deals with the content of texts, authorship identification focuses on the form property of texts...
Signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Dozens of new malicious codes are created every day, and the rate is expected to increase in coming years. To extend the generalization to detect unknown malicious code, heuristic methods are used; however, these are not successful enough. Recently, classification algorithms were used successfully for...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
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