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Learning from data streams has inspired considerable interests in recent years due to its wide applications in the fields such as network intrusion detection, credit fraud identification, spam filtering, and many others. Given the fact that most algorithms developed thus far assume the class distribution of the streaming data is relatively balanced, they will inevitably be confronted with severe performance...
Intention is a mental state and a driving force of human behaviors, which associates with other psychological phenomena, such as belief, desire, and goal. Understanding intention's characteristics and working mechanism has caught the attentions from several disciplines, such as philosophy, psychology, computer science. Although the relationship was proven formally, there always exist some risks of...
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...
In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an...
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...
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