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The classification performance of previous IG algorithm may decline obviously because of the maldistribution of classes and features, due to which an improved text feature selection method UDsIG is proposed. First, we select features by classes to reduce the impact on feature selection when the classes are unevenly distributed. After that, we use feature equilibrium of distribution to decrease the...
Blogosphere has become a hot research field in recent years. As the existing detection algorithm has problems of inefficient feature selection and weak correlation, we propose an algorithm of splog detection based on features relation tree. We could construct the tree according to the correlation of the features, reserving the strong relevance features and removing the weak ones, then prune the redundant...
Bispectrum is an effective measurement to the intrinsic stray feature of radio transmitters, however, it is not feasible to directly exploit bispectra for radio transmitter recognition because of the high dimensionality. In this paper, a novel dimensionality reduction method named structured sparsity preserving projections (SSPP) is proposed for feature extraction. SSPP captures the structured sparse...
A method to reduce feature dimension based on CCA and PCA is proposed. First, using the CCA to fuse the LPC features based on channel model and the MFCC feature based on auditory model to improve the relevance of the two different features; second, utilizing the PCA to further remove redundant features, and reduce the dimension of effective features. To verify the validity of this method, experimental...
In text categorization, feature selection is an effective feature dimension-reduction methods. To solve the problems of unadaptable high original feature space dimension, too much irrelevance, data redundancy and difficulties in selecting a threshold, we propose an improved LAM feature selection algorithm (ILAMFS). Firstly, combining the gold segmentation and the LAM algorithm based on the characteristics...
In this paper, we present a novel feature extraction and classification approach for radio transmitter recognition based on bispectra with tensor representation. Bispectra are quite effective to capture the stray features of radio transmitters in stationary state. Traditional approaches such as integral bispectra usually transform a bispectra matrix into a vector for feature extraction and classification,...
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