A Novel Detection Design via Drift Calibration and Chebyshev Polynomial Weighted Unbalanced Classifier Design

In order to meet the demand for odor detection in the herbal medicine, a multisensor detection system consisting of metal-oxide sensors (MOSs) is designed. With the consideration of possible sensor drift existing in the herbal medicine datasets, a subspace projection method considering multiple constraints including the preservation of discriminative manifold is considered. In addition, discriminant extreme learning machine based on a Chebyshev polynomial weighting (DELM-CPW) is proposed to overcome the negative impact on the calibration recognition result due to the unbalanced number of samples of different classes in the source domain data. Moreover, the odor data of four classes of herbal medicine are collected by our system. They are analyzed with various machine learning algorithms, and the proposed algorithm obtains an accuracy of 0.8393 and an <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score of 0.8313, which are higher than other classical algorithms, it can validate the effectiveness of the designed system and show the potential of the system to be applied in the intelligent identification of herbal medicine.