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

Liu, Miao; Wang, Mingjun; Wang, Jun; Li, Duo

doi:10.1016/j.snb.2012.11.071

Cited by 263 publications

(142 citation statements)

References 63 publications

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“…The multilayer perceptron is a feed forward ANN model that projects input data onto a set of suitable output by using three or more layers of nodes with nonlinear activation functions [58,110]. ANN has been widely used in modeling vegetation and tree species traits that are not linearly predictable in the original remotely sensed variables [41,111].…”

Section: Artificial Neural Network (Ann) Regression Algorithmmentioning

confidence: 99%

“…However, as previously mentioned the conventional empirical methods are constrained by some limitations related to the normal distribution of the response variables and multi-collinearity [26,27]. The use of machine learning methods has therefore regarded as efficient and robust protocols for predicting forest biophysical traits in the field of remote sensing [40][41][42]. Particularly, these methods, which make no assumption on the out response variables distribution, have increasingly offered a better capability to analyze remotely sensed data [43,44].…”

Section: Introductionmentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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Section: Artificial Neural Network (Ann) Regression Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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“…In this respect it is a very useful tool, more universal than most of the known solutions of signal processing in an electronic nose, such as the linear discriminant analysis, principal component analysis (PCA), neuro-fuzzy systems or neural networks [13−17]. On the other hand, the support vector machine was created as a classification tool significantly resistant to noise distorting the input data [12,17,18]. Both methods are compared for the data distorted by the random noise with a normal distribution, zero mean and different variance values.…”

Section: Introductionmentioning

confidence: 99%

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Osowski

Siwek

2017

Metrology and Measurement Systems

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The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

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“…In the past decades, neural networks based on gradient descent algorithms such as back propagation (BP) neural network have been widely applied in modeling, prediction, classification, and regression applications [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A New Extreme Learning Machine Optimized by Particle Swarm Optimization

Zhang¹

2017

2017 International Conference on Computing, Communications and Automation (I3CA 2017)

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Abstract. Extreme Learning Machine (ELM) is a new type of feedforward neural network (FNN).Compared with traditional single hidden layer FNN，ELM possesses higher speed and better performance. Due to the random determination of the input weights and hidden layer biases, ELM may need more hidden neurons to achieve a reasonable accuracy. In this paper, a novel ELM learning algorithm optimized by Particle Swarm Optimization (PSO) which called PSO-ELM is proposed. PSO-ELM uses PSO algorithm to select input weights and hidden layer biases. The experimental results based on SinC benchmarking function and a large number of classification experiments from UCI standard data set show that the new algorithm can produce better generalization performance with less hidden neurons than traditional ELM algorithms and conventional feedforward neural networks.

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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

Cited by 263 publications

References 63 publications

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

A New Extreme Learning Machine Optimized by Particle Swarm Optimization

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