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Obfuscated and encrypted protocols hinder traffic classification by classical techniques such as port analysis or deep packet inspection. Therefore, there is growing interest for classification algorithms based on statistical analysis of the length of the first packets of flows. Most classifiers proposed in literature are based on machine learning techniques and consider each flow independently of...
Hierarchical classification problems have been wide investigated in the past years. The available hierarchical classification methods, which use the top-down level-based scheme, often suffer from the burden of inter-level error transmission. In this paper, an instance-centric hierarchical classification framework based on decision-theoretic rough set model is proposed. The procedure of classification...
This paper presents a study on using a concept feature to detect web phishing problem. Following the features introduced in Carnegie Mellon Anti-phishing and Network Analysis Tool (CANTINA), we applied additional domain top-page similarity feature to a machine learning based phishing detection system. We preliminarily experimented with a small set of 200 web data, consisting of 100 phishing webs and...
Tangent vectors are one of the best tools for learning variability in handwritten digits. Many research works indicate that tangent vectors provide a significant improvement of accuracy especially when used with SVM classifiers. However, since they are based on the use of affine transformations they substantially extend the runtime. In addition, the user should adequately select the transformations...
One of the main difficulties in machine learning is how to solve large-scale problems effectively, and the labeled data are limited and fairly expensive to obtain. In this paper a new semi-supervised SVM algorithm is proposed. It applies tri-training to improve SVM. The semi-supervised SVM makes use of the large number of unlabeled data to modify the classifiers iteratively. Although tri-training...
The problem of PU Learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We address this problem through a novel approach: reducing it to the problem of learning classifiers for some meaningful multivariate performance measures. In particular, we show how a powerful machine learning algorithm,...
In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB),...
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