In the presence of large dimensional datasets that contain many irrelevant features(variables), dimensionality reduction algorithms have proven to be useful in removing features with low variance and combine features with high correlation . In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold. To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}. We cluster features first based on \Delta\mathbf{x} and then using the entropy of features. Finally, a feature is selected from each sub-cluster based on its weight and entropy. } The effectiveness of our method has been verified by performing a series of comparisons with  state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features. The results show that our method is favorable in various aspects compared to state-of-the-art feature selection methods.\e
A central problem in machine learning and pattern recognition is the process of recognizing the most important features . In this paper, we provide a new feature selection method (DRPT) that consists of first removing the irrelevant features and then detecting correlations between the remaining features. Let D= A\mid \mathbf{b}] be a dataset, where \mathbf{b the corresponding column (feature). We define a threshold based on the local maxima of \mathbf{x and remove those features whose weights are smaller than the threshold. To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde A of A. We prove that correlations are encoded in \Delta\mathbf{x=\mid x -}\mathbf{x}\mid , where\tilde{\mathbf{x}} is the least quares solution of\tilde A\tilde{\mathbf{x}}=\mathbf{b. We cluster features first based on \Delta\mathbf{x} and then using the entropy of features. Finally, a feature is selected from each sub-cluster based on its weight and entropy. } The effectiveness of DRPT has been verified by performing a series of comparisons with seven state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features. The results show that , over all, the performance of DRPT is favorable in several aspects compared to each feature selection algorithm.\e
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In the presence of large dimensional datasets that contain many irrelevant features(variables), dimensionality reduction algorithms have proven to be useful in removing features with low variance and combine features with high correlation
A central problem in machine learning and pattern recognition is the process of recognizing the most important features
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propose
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which uses singular value decomposition of a matrix and
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method of least squares to remove the irrelevant features and detect correlations between the remaining features .
corresponding column (feature). We define a threshold based on the local maxima of \mathbf{x
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A of A. We prove that correlations are encoded in \Delta\mathbf{x
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our method
DRPT
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, over all, the performance of DRPT
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various
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state-of-the-art feature selection methods.
each feature selection algorithm.