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This paper proposes a novel feature ranking method, DensityRank, based on kernel estimation on the feature spaces to improve the classification performance. As the availability of raw data in many of today's applications continues to grow at an explosive rate, it is critical to assess the learning capabilities of different features and select the important subset of features to improve learning accuracy...
Recent years have witnessed an incredibly increasing interest in the topic of stream data mining. Despite the great success having been achieved, current approaches generally assume that the class distribution of the stream data is relatively balanced. However, in applications such as network intrusion detection, credit fraud detection, spam classification, and many others, the class distribution...
This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that...
In this paper, we propose a novel method for incremental semi-supervised learning. Unlike the traditional way of incremental learning or semi-supervised learning, we try to answer a more challenging question: given inadequate labeled training data, can one use the unlabeled testing data to improve the learning and prediction accuracy? The objective here is to reinforce the learning system trained...
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