Comprehensive review on twin support vector machines

Tanveer, M.; Rajani, T.; Rastogi, Reshma; Shao, Yuan‐Hai; Ganaie, M. A.

doi:10.1007/s10479-022-04575-w

Cited by 132 publications

(43 citation statements)

References 289 publications

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“…Widely acknowledged for its exceptional ability to generalize and solve nonlinear problems, the SVM model is a popular supervised learning technique. Its utilization of the structural risk minimization (SRM) principle provides improved generalization and reduces errors in the training phase . Enhanced generalization in the presence of noise was achieved through the robust penalization of all samples by the SVM .…”

Section: Methodsmentioning

confidence: 99%

“…Its utilization of the structural risk minimization (SRM) principle provides improved generalization and reduces errors in the training phase. 31 Enhanced generalization in the presence of noise was achieved through the robust penalization of all samples by the SVM. 32 While the SVM has demonstrated superior performance compared with most other systems, it encounters limitations in dealing with complex data, primarily due to the high computational cost of solving quadratic programming problems (QPPs) and its strong reliance on the selection of kernel functions and their parameters.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Gandomzadeh,

Rohani,

Abbaspour-Fard

2023

Ind. Eng. Chem. Res.

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Section: Methodsmentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Gandomzadeh,

Rohani,

Abbaspour-Fard

2023

Ind. Eng. Chem. Res.

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“…This model which is based on the idea of structural risk minimization (SRM), has been utilized successfully for classification and regression (Tanveer et al, 2021). SVM can be applied either for linear separable problems or more complex nonlinear situations because of its possibility of generating assembled curved boundaries (Broséus et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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“…As a binary classification method, Support Vector Machines (SVM) is extensively applicated in statistical classification and regression analysis [49]. Possessing the merits of small generalization error and low computational cost, SVM is particularly applicable to the classification of small samples with complex features [50].…”

Section: E Sentiment Classificationmentioning

confidence: 99%

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

Gui-jun

Zhao

2022

IEEE Access

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With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers' perceptions and attitudes from massive comments has become a hot-button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers' reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews' emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers' demands, thereby strongly supporting future product improvement and marketing strategy optimization.INDEX TERMS e-commerce product experience, fusion method, machine learning, sentiment analysis, sentiment dictionary.

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Comprehensive review on twin support vector machines

Cited by 132 publications

References 289 publications

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

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