In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity ...
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In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (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 including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM
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 ...
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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 intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms
In this paper, we propose a tree based regression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. By function approximation based on multivariable polynomial regression an...
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In this paper, we present our Grid-based decision tree architecture, with the intention of applying it to both parallel and sequential algorithms. Also, we show that, based on the scope and model of data mining applie...
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In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there...
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Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional bound...
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The co-allocation architecture was developed in order to enable parallel downloads of datasets from multiple servers. Several co-allocation strategies have been coupled and used to exploit rate differences among vario...
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In this paper, we present BioGrid, a novel computing resource that combines advantages of grid computing technology with bioinformatics parallel applications. The grid environment permits the sharing of a large amount...
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In this paper, we investigate the recent popular computing technique called Grid computing, and use video conversion and 3D rendering applications to demonstrate this technology's effectiveness and high performanc...
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The Data Grid enables the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for solving large-scale data intensive scientific applications. Such tec...
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