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Feature selection method based on multiple centrifuge models
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DOI：10.1007/s10586-017-0812-9.png)
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Abstract
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High-dimension of feature space in text classification is a major problem of it. Feature selection is an effective method for feature reduction. A multiple centrifuge models based feature selection method is put forward in the view of the hypothesis that the same documents have core feature set in the text classification and the classes of the same high-frequency feature words of document have affinity. The proposed feature selection algorithm made a lot of innovation ideas in the field of feature reduction which improve the values of the low-frequency features in classification meanwhile ensuring the classification effect. The experiments in the Reuters-21578 corpus show that this method has better classification effect, and effectively improves the utilization of medium or low frequency features which have strong classification ability.
Keywords:
Centrifuge model
Qrderly whole class feature vector
Centroid feature set
Centrifuge matrix
Torque adjoint matrix
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