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Multi_label learning and application is a new hot issue in machine learning and data mining recently. In multi_label learning, a training set is composed of instances, each is associated with a set of labels, and the task is to predict all the appropriate labels of unseen instances. In this paper, the authors research on proposing Apriori algorithm to search the relationship between all labels. In...
Multi_label learning and application is a new hot issue in machine learning and data mining recently. In multi_label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, authors research on classifying multi_label data based on...
Association rules are import basis of describing Web users' behavior characteristic. Traditional algorithms of Web association rules mining, based on statistics, usually pays attention to the analysis on existing data,they can't offer effective predictive means and optimizing measure and can not find out the latent and possible rules. This paper presents a kind of system of the Web association rules...
Multi-label learning is a hot spot in machine learning and data mining. The multi-label learning model can predict one or more labels for a test instance. PT5 is a effective method to solving multi-label learning problem, it is critical to set an optimal threshold in this method. More error labels will be predicted if a lower threshold was set, and the labels will be predicted not entire if a big...
The traditional data mining algorithms behaves undesirable in the instance of imbalanced data sets, as the distribution of the data sets is not taken into consideration when these algorithms are designed. This paper describes the Naive Bayes classification mechanism, and then points out that, random re-sampling methods not to be able to improve its performance. A negative-case-pruning Naive Bayes...
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