Feature selection is an effective way of improving classification, reducing feature dimension, and speeding up computation. This work studies a reported support vector machine (SVM) based method of feature selection. Our results reveal discrepancies in both its feature ranking and feature selection schemes. Modifications are thus made on which our SVM-based method of feature selection is proposed. Using the weighting fusion technique and the one-against-all approach, our binary model has been extensively updated for multi-class classification problems. Three benchmark datasets are employed to demonstrate the performance of the proposed method. The multi-class model of the proposed method is also used for feature selection in planetary gear damage degree classification. The results of all datasets exhibit the consistently effective classification made possible by the proposed method.

The effects of 1.0 MeV electron irradiation on the dark conductivity and amorphous network of heavily phosphorus-doped a-Si : H films have been studied. The electron irradiation leads to a strong decrease by about two orders of magnitude in the dark conductivity of heavily phosphorus-doped a-Si : H films and the degradation comes to a saturation. The evolutions of the amorphous silicon network of heavily doped a-Si : H films caused by electron irradiation were investigated by Raman spectroscopy. The observed decrease in the amorphous silicon network order in the short and intermediate range suggests that the electron irradiation induces structural defects in the films. This defect creation also tends to saturate after a long irradiation time.

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