method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and support vector machines (SVMs) with the one-versus-rest method serve as a fitness function of PSO for the classificat...

A novel method of feature selection combined with sample selection is proposed to select discriminant features in this paper. Based on support vector machine trained on training set, the samples excluding the misclassified samples and support vector samples are used to select informative features during the procedure of recursive feature selection. The feature selection method is applied to sev...

In this study, a method for classification of tumor sample microarray data based on support vector machines is presented. Different possibilities for data processing, gene selection and support vector machine classification are recited. The performance of support vector machine classification is compared to that of linear discriminant analysis and decision tree -based classifiers.

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved, available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. A reliable selection me...

Self-Selection and the Efficiency of Tournaments When exogenously imposed, rank-order tournaments have incentive properties but their overall efficiency is reduced by a high variance in performance (Bull, Schotter, and Weigelt 1987). However, since the efficiency of performance-related pay is attributable both to its incentive effect and to its selection effect among employees (Lazear, 2000), i...

Using linked employer-employee data, this study measures and decomposes the differences in the earnings distribution between male and female employees in Germany. I extend the traditional decomposition to disentangle the effect of human capital characteristics and the effect of firm characteristics in explaining the gender wage gap. Furthermore, I implement the decomposition across the whole wa...

MOTIVATION The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classif...