Zongxing Xie

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In this paper, the rare event detection issue in video event detection is addressed through the proposed data mining framework which can be generalized to be domain independent. The fully automatic process via the combination of distance-based and rule-based data mining techniques can greatly reduce the number of negative (non-event) instances and the(More)
—In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of(More)
Recently, network security has become an extremely vital issue that beckons the development of accurate and efficient solutions capable of effectively defending our network systems and the valuable information journeying through them. In this article, a distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to(More)
In this paper, a novel agent-based distributed intrusion detection system (IDS) is proposed, which integrates the desirable features provided by the distributed agent-based design methodology with the high accuracy and speed response of the Principal Component Classifier (PCC). Experimental results have shown that the PCC lightweight anomaly detection(More)
In this paper, a novel supervised classification approach called Collateral Representative Subspace Projection Mod-eling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits(More)
In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce(More)
The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network(More)
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