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In this paper, we proposed the designing method using ensemble learning method (e.g. AdaBoosting method) with feature selection to solve the trade-off problem between accuracy and generalization.
The performance of band selection method with feature absorption described in Section "Band selection" is assessed in the following work.
Another direction is to combine the proposed method with feature vectors that are found via deep learning [31 34].
Because of excluding the effects of noise and resolving the multicollinearity among input variables, the method with feature extraction by using partial least squares has some features compared to other methods, such as the less time spent and higher accuracy.
In this study, we described a scaffold (proof-of-concept) adapted from spectroscopy to quantify Cordyceps sinensis and Ganoderma lucidum in a popular Cordyceps sinensis /Ganoderma lucidum -enriched health beverage by utilizing flow-injection/mass spectrometry/artificial neural network (FI/MS/ANN) model fingerprinting method with feature selection capability.
The entire work flow of our rule discovery method with feature space projection is summarized in Figure 3.
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Our main interest was to asses whether a method with feature-based multiple models can improve prediction performance compared to methods employing only a single model.
In this section, we compared the three proposed methods with feature selection to a pure SVM to measure the effectiveness of the additional feature selection method.
For methods with feature vector (allowed to contain some spectrum-specific quantities) updated on-the-fly [6], [8], the spectrum-specific bias may be partially compensated, but not giving rise to spectrum-specific statistics.
Results of classification methods with feature selection like GAPLSSVM, GAPCASVM and GAPPSVM are better than those of the corresponding feature extraction methods without feature selection like PLSSVM, PCASVM and PPSVM on average.
Results of classification methods with feature selection like GAPLSKNN, GAPCAKNN and GAPPKNN are better than those of the corresponding feature extraction methods without feature selection like PLSKNN, PCAKNN and PPKNN on average and each cases.
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