PSO-based method for SVM classification on skewed data sets

Cervantes, Jair; García-Lamont, Farid; Rodríguez, Lisbeth; Lpez, Asdrbal; Castilla, Jos Ruiz; Trueba, Adrián

doi:10.1016/j.neucom.2016.10.041

Cited by 71 publications

(21 citation statements)

References 42 publications

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“…Firstly, it can find an optimal hyperplane with the highest classification boundary in the n-dimensional feature space. This prevents the classifier from falling into local minima [63], which is the case for ANN. Secondly, SVM can minimize unseen errors in training samples [78] and, thus, a higher classification accuracy [79].…”

Section: Comparison Of Different Classifier Resultsmentioning

confidence: 99%

“…If the samples are linearly separable, a linear discriminant function is established by constructing the classification surface to ensure the maximum distance between the samples. If the samples are linearly inseparable, the SVM projects the training samples to a high-dimensional space and finds the optimal classifying hyperplane [63].…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

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Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Zhou

Chen

et al. 2019

Forests

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Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.

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Section: Comparison Of Different Classifier Resultsmentioning

confidence: 99%

Section: Support Vector Machine Classifiermentioning

confidence: 99%

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Zhou

Chen

et al. 2019

Forests

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“…SVM classification can be obtained by Equation (11) 5 Wireless Communications and Mobile Computing related parameters of the ith feature vectors. The mapped sample inner product is replaced by the kernel function, which can effectively solve the linear inseparable sample classification problem [25].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Fruit Classification Model Based on Residual Filtering Network for Smart Community Robot

Chen

Sun

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of computer vision and robot technology, smart community robots based on artificial intelligence technology have been widely used in smart cities. Considering the process of feature extraction in fruit classification is very complicated. And manual feature extraction has low reliability and high randomness. Therefore, a method of residual filtering network (RFN) and support vector machine (SVM) for fruit classification is proposed in this paper. The classification of fruits includes two stages. In the first stage, RFN is used to extract features. The network consists of Gabor filter and residual block. In the second stage, SVM is used to classify fruit features extracted by RFN. In addition, a performance estimate for the training process carried out by the K -fold cross-validation method. The performance of this method is assessed with the accuracy, recall, F1 score, and precision. The accuracy of this method on the Fruits-360 dataset is 99.955%. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed method over existing systems on fruit classification.

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“…The kernel function parameter g represents the kernel function parameter γ, k in the g represents the number of attributes in the input data. Thus, the hit rate of the roll gap value prediction model is governed by these two parameters [20][21][22][23][24][25][26][27][28][29].…”

Section: Pso-svm Modelmentioning

confidence: 99%

Research and Application of a Rolling Gap Prediction Model in Continuous Casting

Lei

2019

Metals

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Control of the roll gap of the caster segment is one of the key parameters for ensuring the quality of a slab in continuous casting. In order to improve the precision and timeliness of the roll gap value control, we proposed a rolling gap value prediction (RGVP) method based on the continuous casting process parameters. The process parameters collected from the continuous casting production site were first dimension-reduced using principal component analysis (PCA); 15 process parameters were chosen for reduction. Second, a support vector machine (SVM) model using particle swarm optimization (PSO) was proposed to optimize the parameters and perform roll gap prediction. The experimental results and practical application of the models has indicated that the method proposed in this paper provides a new approach for the prediction of roll gap value.

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PSO-based method for SVM classification on skewed data sets

Cited by 71 publications

References 42 publications

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Fruit Classification Model Based on Residual Filtering Network for Smart Community Robot

Research and Application of a Rolling Gap Prediction Model in Continuous Casting

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