Intuitionistic Fuzzy Twin Support Vector Machines

Rezvani, Salim; Wang, Xizhao; Pourpanah, Farhad

doi:10.1109/tfuzz.2019.2893863

Cited by 132 publications

(29 citation statements)

References 48 publications

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“…In this section, we investigate the effectiveness and generalization capability of the proposed method on artificial and UCI datasets, and we compare IFLap-TSVM with Lap-TSVM [20], IFTSVM [24] and TSVM [11].…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, combining the TSVM with membership function can not only improve computational efficiency but also pursue robust performance. In recent years, intuitionwastic fuzzy twin support vector machine (IFTSVM) [24] has been proposedd which assigns a pair of membership and nonmembership functions to every training sample. These two functions help the IFTSVM to reduce the influence of nowases and identify support vectors from nowases.…”

Section: Introductionmentioning

confidence: 99%

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Zhou

Bai

Guo

et al. 2021

J. Oper. Res. Soc. China

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In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Zhou

Bai

Guo

et al. 2021

J. Oper. Res. Soc. China

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show abstract

“…Developing a model between input (videos) and outputs (human physiological signals) is another important aspect of future work in this regard. For this purpose, we can benefit from different tools such as machine learning [ 37 - 39 ] and fractional-based mathematical equations [ 40 ]. Such analysis could allow for predicting human conditions (physiological signals) before exposure to different stimuli, providing guidance on the types of videos and characteristics of videos that are most likely to arouse the attention of students and facilitate learning.…”

Section: Discussionmentioning

confidence: 99%

Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis

2020

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Background Education and learning are the most important goals of all universities. For this purpose, lecturers use various tools to grab the attention of students and improve their learning ability. Virtual reality refers to the subjective sensory experience of being immersed in a computer-mediated world, and has recently been implemented in learning environments. Objective The aim of this study was to analyze the effect of a virtual reality condition on students’ learning ability and physiological state. Methods Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos. Results The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos. Conclusions A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.

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“…We adopt the Support Vector Machine which has the strong feature representation ability as the backbone of our classification algorithm. Since the SVM had proved as a power tool in machine leaning and data mining with the strong robustness and classification ability [36]. In practice, the high sensitivity and efficient detect COVID-19 cases is the most critical task.…”

Section: Classification Blockmentioning

confidence: 99%

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images

Zhou

Zhang

et al. 2021

IEEE Access

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As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.

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Intuitionistic Fuzzy Twin Support Vector Machines

Cited by 132 publications

References 48 publications

Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images

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