Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

Gupta, Deepak; Borah, Parashjyoti; Sharma, Usha; Prasad, Mukesh

doi:10.1007/s00521-021-05866-2

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Medical data classification has been extended to study a novel random vector functional link [26] and a novel random vector functional link with ε-insensitive Huber loss function [27]. A fuzzy-based Lagrangian twin parametricmargin support vector machine [28] reduced the effect of the outliers in medical data.…”

Section: Discussionmentioning

confidence: 99%

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Araveeporn

2022

International Journal of Mathematics and Mathematical Sciences

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Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.

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Section: Discussionmentioning

confidence: 99%

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Araveeporn

2022

International Journal of Mathematics and Mathematical Sciences

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“…SVM algorithms are based on the concept of mapping data points from low-dimensional into high-dimensional space [21]. Support vector machines are sensitive to outliers and noise [57,58]. e schematic diagram of SVM is drawn in Figure 3.…”

Section: Svmmentioning

confidence: 99%

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Edwine

Wang

Song

et al. 2022

Mathematical Problems in Engineering

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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.

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“…With the support of speech sensors and other electronic technologies, various speech electronic terminals have become an essential tool in daily office, transportation, business and medical activities. However, in traditional speech signal processing techniques the importance of the emotional information contained in the speech signal is severely underestimated and is even considered as noise that is eliminated by various regularised pattern anomalies processing techniques, resulting in biased perception by the listener [2].…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

2022

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Language is an important part of everyday information communication, and automatic speech emotion information recognition technology has broad application prospects in education, services and medicine. The purpose of this paper is to study speech emotion recognition by specific fuzzy support vector machines. The applicability of fuzzy theory for solving speech emotion states is investigated and the theory of support vector machines, fuzzy support vector machines, is introduced to analyse speech emotion feature extraction in terms of both short-time energy and resonance peaks. The performance of SVM and FSVM methods are compared using Emo-DB corpus tests and it is found that the FSVM method has a higher recognition rate than the SVM method.

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Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis

Cited by 17 publications

References 38 publications

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Speech Emotion Recognition Based on Fuzzy Support Vector Machine

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