Cancer classification and identification are major areas in medical research. DNA microarrays could provide useful information for cancer classification at the gene expression level. The number of genes in a microarra...
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Cancer classification and identification are major areas in medical research. DNA microarrays could provide useful information for cancer classification at the gene expression level. The number of genes in a microarray is always several thousands while the number of training samples always several dozens. In such case most of the machine learning models suffer from the overfitting and it is necessary to select a handful of most informative genes. An adaptive and iterative gene selection algorithm based on least squares support vector machines is proposed in this paper. The algorithm adopts sequential forward selection search scheme. The number of selected genes can be determined adaptively. The total number of genes processed by the proposed algorithm is smaller than that processed by other algorithms using support vector machines. Results of numerical experiments show that the proposed algorithm trains fast and achieves comparable performance on two well-known benchmark problems.
The proxy cache for streaming media is the important method to economize the resources of the Internet. The cache policies influence the effect for proxy cache. In this paper, based on the client's request rate, c...
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The optimization of job-shop scheduling is very important because of its theoretical and practical significance. This paper proposes an efficient scheduling method based on artificial immune systems. In the proposed m...
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We systematically propose a dual-phase algorithm, DualRank, to mine the optimal profit in retailing market. DualRank algorithm has two major phases which are called mining general profit phase and optimizing profit ph...
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Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequences. In this paper, an immune particle swarm optimization (IPSO) is proposed, which is based on the models o...
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To solve the problem that when patterns are long, frequent sequential patterns mining may generate an exponential number of results, which often makes decision-makers perplexed for there is too much useless repeated i...
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A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique-the random sample consensus (RANSAC) to discard outliers in a...
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This paper is concerned with solution of the consistent fundamental matrix estimation in a quadratic measurement error model. First an extended system for determining the estimator is proposed, and an efficient implem...
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In this paper, we analyze the method of support-confidence framework when mining association rules. In order to avoid the limitation in the criterion, we propose a new method of match as the substitution of confidence...
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Under the framework of LPU (learning from positive data and unlabeled data), this paper originally proposes a three-step algorithm. First, Co-Training is employed for filtering out the "suspect positive" dat...
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