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A support vector machine (SVM) [32] was used for feature classification.
The major dissimilarity between LDA and PCA is that, generally, PCA is used for feature classification, whereas LDA is used for data classification.
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In [27], a deep belief network (DBN) was used for feature extraction and classification.
Given a spectrum, the DWT was applied to produce a set of wavelet coefficients which are further used for feature selection and classification.
In [27, 28], a deep belief network (DBN) [29] was used for feature selection, learning and classification.
The data adjusted by the Combat was then used for feature selection procedure in classification and differential methylation analysis.
These results are used for feature extraction which helps in classification of PQ events.
In this paper PSE used for feature extraction of PQ events for classification purpose.
We compared our methods against SVM-RFE (wrapper) and MRMR (filter) approaches that have been used for classification and feature selection of large dimensional biological data.
Usually, the SVM classifier is used for classification of features into two classes.
The features used for the classification are distance from center, circularities, compactness, major axis width and length, elongation, Heywood diameter, the intensity average, and its standard deviation.
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