期刊
SENSORS AND ACTUATORS B-CHEMICAL
卷 177, 期 -, 页码 970-980出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2012.11.071
关键词
Random forest; Support vector machine; Back propagation neural network; Pattern recognition; Classification; Electronic tongue
资金
- Chinese National Foundation of Nature and Science [31071548, 31201368]
- Research Fund for the Doctoral Program of Chinese National Higher Education [20100101110133]
Random forest (RF) has been proposed on the basis of classification and regression trees (CART) with ensemble learning strategy by Breiman in 2001. In this paper, RF is introduced and investigated for electronic tongue (E-tongue) data processing. The experiments were designed for type and brand recognition of orange beverage and Chinese vinegar by an E-tongue with seven potentiometric sensors and an Ag/AgCl reference electrode. Principal component analysis (PCA) was used to visualize the distribution of total samples of each data set. Back propagation neural network (BPNN) and support vector machine (SVM), as comparative methods, were also employed to deal with four data sets. Five-fold cross-validation (CV) with twenty replications was applied during modeling and an external testing set was employed to validate the prediction performance of models. The average correct rates (CR) on CV sets of the four data sets performed by BPNN, SVM and RF were 86.68%, 66.45% and 99.07%, respectively. RF has been proved to outperform BPNN and SVM, and has some advantages in such cases, because it can deal with classification problems of unbalanced, multiclass and small sample data without data preprocessing procedures. These results suggest that RF may be a promising pattern recognition method for E-tongues. (c) 2012 Elsevier B.V. All rights reserved.
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