Time-weighted Fuzzy Support Vector Machines for classification in changing environments

Maldonado, Sebastián; López, Julio; Vairetti, Carla

doi:10.1016/j.ins.2021.01.070

Cited by 22 publications

(11 citation statements)

References 37 publications

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“…Note, a smaller a n decreases the influence of the slack vector ρ n so that the corresponding sample g n is deemed less substantial. The Lagrangian [ 30 ] is attained similarly as …”

Section: Methodsmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

et al. 2022

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis . Our study investigates the recognition of secondary pulmonary (SPTB). A novel F3 model is proposed. The first F means using a four-direction varying-distance gray-level co-occurrence matrix (FDVDGLCM) to analyze the chest CT images; the second F means a five-property feature set (FPFS) from the FDVDGLCM results; the third F means fuzzy support vector machine (FSVM). Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves a sensitivity of 93.68% ± 1.75%, a specificity of 94.17% ± 1.68%, a precision of 94.17% ± 1.55%, an accuracy of 93.92% ± 1.05%, an F1 score of 93.91% ± 1.07%, an MCC of 87.88% ± 2.09%, and an FMI of 93.92% ± 1.06%. The AUC is 0.9624. The FSVM can give better performance than ordinary SVM. The proposed F3 model is superior to six state-of-the-art SPTB recognition models.

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“…Note, a smaller a n decreases the influence of the slack vector ρ n so that the corresponding sample g n is deemed less substantial. The Lagrangian [ 30 ] is attained similarly as …”

Section: Methodsmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

et al. 2022

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“…Fuzzy sets are used to describe the uncertainty of things, and are widely used in fuzzy reasoning, fuzzy intelligent decision-making systems and other fields, and thus have received widespread attention (see Bera and Pal [1] and [2], Islam and Pal [3], Samanta1 et al [4], Amanathulla et al [5], Pal et al [6], Prabakaran et al [7], Bagherinia et al [8], Gonzalez et al [9], and Maldonado et al [10]). On the other hand, graphs are an effective tool to describe the structured data.…”

Section: Introductionmentioning

confidence: 99%

Domination of Bipolar Fuzzy Graphs in Various Settings

Gong

Hua

Gao

2021

Int J Comput Intell Syst

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Bipolar fuzzy sets are used to describe the positive and negative of the uncertainty of objects, and the bipolar fuzzy graphs are used to characterize the structural relationship between uncertain concepts in which the vertices and edges are assigned positive and negative membership function values to feature the opposite uncertainty elevation. The dominating set is the control set of vertices in the graph structure and it occupies a critical position in graph analysis. This paper mainly contributes to extending the concept of domination in the fuzzy graph to the bipolar frameworks and obtaining the related expanded concepts of a variety of bipolar fuzzy graphs. Meanwhile, the approaches to obtain the specific dominating sets are presented. Finally, a numeral example on city data in Yunnan Province is presented to explain the computing of domination in bipolar fuzzy graph in the specific application.

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“…In terms of the choice of the modelling method, existing modelling methods include principal component regression (PCR) [35,36], partial least squares regression (PLSR) analysis [35,37], support vector machines (SVMs) [38,39], back-propagation neural networks (BPNNs) [40], random forests (RFs) [41], and extreme learning machines (ELMs) [42]. Chengwen Chang et al used principal component regression to predict 33 chemical, physical, and biochemical properties of 802 soil samples collected from four major land resource areas with a prediction accuracy of 0.8 [36].…”

Section: Introductionmentioning

confidence: 99%

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Liu

Bao

et al. 2021

Minerals

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To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

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Time-weighted Fuzzy Support Vector Machines for classification in changing environments

Cited by 22 publications

References 37 publications

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Domination of Bipolar Fuzzy Graphs in Various Settings

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

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