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Classifying biomedical spectra is often difficult due to the bir voluminous nature; typically, only a small subset of spectral features is discriminatory, while the large majority tends to have a confounding effect on pattern classifiers. We present a two-pronged approach to dealing with this issue. First, we describe an iterative technique whereby many classifier instances operate on different feature...
In this paper, we present a new method for dealing with feature subset selection based on fuzzy methods and consistency measures for handling classification problems. In fuzzy classifier systems the classification is obtained by a number of fuzzy if-then rules including linguistic terms such as low and high that fuzzify each feature. First, we project the original data set into a fuzzy space, then...
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...
One of the most important techniques in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. The goal is to find a...
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