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This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each(More)
MOTIVATION In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known(More)
To further understand functional connectivity in the brain, we need to identify the coupling direction between neuronal signals recorded from different brain areas. In this paper, we present a novel methodology based on permutation analysis and conditional mutual information for estimation of a directionality index between two neuronal populations. First,(More)
Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set(More)
Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents. This problem has been studied in information retrieval, machine learning and data mining. So far, many effective techniques have been proposed. However, most techniques are based on some underlying models and/or assumptions.(More)
Many real-world applications in time series classification fall into the class of positive and unlabeled (PU) learning. Furthermore, in many of these applications, not only are the negative examples absent, the positive examples available for learning can also be rather limited. As such, several PU learning algorithms for time series classification have(More)
Cost-sensitive classification is an attractive topic in data mining. Although genetic programming (GP) technique has been applied to general classification , to our knowledge, it has not been exploited to address cost-sensitive classification in the literature, where the costs of misclassification errors are non-uniform. To investigate the applicability of(More)