Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar
文献类型：期刊论文
| 作者 | Liu, Miao1,3; Wang, Mingjun2; Wang, Jun1; Li, Duo3 |
| 刊名 | SENSORS AND ACTUATORS B-CHEMICAL
 |
| 出版日期 | 2013-02-01 |
| 卷号 | 177页码:970-980 |
| 关键词 | Random forest Support vector machine Back propagation neural network Pattern recognition Classification Electronic tongue |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
| 类目[WOS] | Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation |
| 研究领域[WOS] | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 关键词[WOS] | SENSOR ARRAY DATA ; PATTERN-RECOGNITION ; COMPOUND CLASSIFICATION ; BOVINE-MILK ; GREEN TEA ; DISCRIMINATION ; TOOL ; CHEMOMETRICS ; WINES ; IDENTIFICATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000315751000129 |
| 源URL | [http://124.16.173.210/handle/834782/1260]  |
| 专题 | 天津工业生物技术研究所_结构生物信息学和整合系统生物学实验室 宋江宁_期刊论文 |
| 作者单位 | 1.Zhejiang Univ, Dept Biosyst Engn, Hangzhou 310058, Zhejiang, Peoples R China 2.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Tianjin 300308, Peoples R China 3.Zhejiang Univ, Dept Food Sci & Nutr, Hangzhou 310058, Zhejiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Miao,Wang, Mingjun,Wang, Jun,et al. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar[J]. SENSORS AND ACTUATORS B-CHEMICAL,2013,177:970-980. |
| APA | Liu, Miao,Wang, Mingjun,Wang, Jun,&Li, Duo.(2013).Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar.SENSORS AND ACTUATORS B-CHEMICAL,177,970-980. |
| MLA | Liu, Miao,et al."Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar".SENSORS AND ACTUATORS B-CHEMICAL 177(2013):970-980. |
入库方式： OAI收割
来源：天津工业生物技术研究所
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