Hualong Bu

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Semi-supervised learning mechanism requires new feature selection methods to work on unlabeled samples. Traditional researches deal it with the help of “filter-type” semi-feature selection mechanism, which may not work well for classification tasks. Genetic algorithm is one of widely used “wrapper-type” supervised feature(More)
Many Semi-supervised learning applications require a feature selection method to deal with the unlabeled samples. Traditional researches deal it either with the “filter-type” feature selection mechanism, which may not work well for classification tasks or “wrapper” mechanism, which need high computational cost. Here we proposed a(More)
Kernel Partial least squares method can obtain nonlinear novel features for further classification and other tasks, the dimension of extracted kernel space is usually very high, there still may contain irrelevant and redundant features, so using feature selection to select the most discriminative and informative features for classification or data analysis(More)
Feature extraction is an important issue for analysis of gene expression microarray data, of which principle component analysis (PCA) is one of the frequently used methods, and in the previous works, the top several principle components are selected for modeling according to the descending order of eigenvalues. In this paper, we argue that not all the first(More)
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