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This study proposes a systematic statistical method called power analysis to find the minimum number of samples required to train the classifier with statistical stability so as to get good classification accuracy.
To solve the accuracy/diversity dilemma in AdaBoost and get good classification performance, the training error threshold is regulated to adjust the diversity of classifier, and the parameters of SVM (regularization parameter C and Gaussian width σ) are changed to control the accuracy of classifier.
According to Table 1, all the three kinds of original features get good classification performance, and OE features gain the best performance.
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