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The ever growing presence of data led to a large number of proposed algorithms for classification and especially decision trees over the last years. Recently, it has been shown that decision trees outperform traditional approaches also on limited data. Therefore, increasing the decision tree classification accuracy yields better performance on both huge and moderate sized datasets. This paper proposes...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
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...
Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. It is used to find an optimal subset to reduce computational cost, increase the classification accuracy and improve result comprehensibility. In this paper, a weighted distance learning approach is introduced to minimize Leaving-One-Out classification error using a gradient descent...
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),...
Mislabeling unlabeled data during the learning process is an inevitable problem for the co-training style semi-supervised learning. In this paper, the participatory learning cognition paradigm is instantiated through employing the data editing as acceptance unit and designing an arousal strategy of data editing as critic unit. Then, this participatory learning is equipped into each individual classifier...
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