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The purpose of the feature selection is to eliminate insignificant features from entire dataset and simultaneously to keep the class discriminatory information for classification problems. Many feature selection algorithms have been proposed to measure the relevance and redundancy of the features and class variables. In this paper, we proposed an improved feature selection algorithm based on maximum...
The high dimensionality of the text categorization raises big hurdles in applying many sophisticated learning algorithms to the text categorization. Feature selection, which reduces the number of features that represent documents, is an absolute requirement in text categorization. In this paper, we proposed a feature selection method, which improved the performance of the Ambiguity Measure feature...
Many feature selection methods have been proposed in recent years, but there is little work concerning the evaluation of the performances with respect to different feature selection methods especially when the ground truth map is unavailable. In this paper, a new method called quantitative multivariate correlation analysis (QMCA) is proposed, which provides a quantitative measure of the useful information...
Feature selection is an important problem for pattern classification systems. Mutual information is a good indicator of relevance between variables, and has been used as a measure in several feature selection algorithms. Because the mutual information could not be calculated directly for continuous data sets in max-relevance and min-redundancy (mRMR) algorithm, here we combine the mRMR algorithm with...
Selecting suitable features is very crucial for achieving successful classification of land cover types. This paper presents a comparative study of three typical feature selection methods for the task of regional land cover classification using MODIS data. Comparison results have shown that Branch and Bound is the best for land cover classification with MODIS data, while ReliefF and mRMR achieve nearly...
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