Fast Laplacian twin support vector machine with active learning for pattern classification

Rastogi, Reshma; Sharma, Sweta

doi:10.1016/j.asoc.2018.10.042

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…This labeled database is simultaneously used by the ML engine to train and improve the ML model. For example, Rastogi and Sharma (2019) propose to carefully select points from a database to label them and train a Laplacian twin support vector machine, which is a classifier able to learn from both labeled and unlabeled data. Similarly, Martinez Arellano and Ratchev (2019) propose an application of pooled-based active learning in an Industry 4.0 context.…”

Section: Data Access Scenariosmentioning

confidence: 99%

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Chabanet

El-Haouzi

Thomas

2021

Computers in Industry

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Although digital simulations are becoming increasingly important in the industrial world owing to the transition toward Industry 4.0, as well as the development of digital twin technologies, they have become increasingly computationally intensive. Many authors have proposed the use of Machine Learning (ML) metamodels to alleviate this cost and take advantage of the enormous amount of data that are currently available in industry. In an industrial context, it is necessary to continuously train predictive models integrated into decision support systems to ensure the consistency of their prediction quality over time. This led the authors to investigate Active Learning (AL) concepts in the particular context of the sawmilling industry. In this paper, a method based on AL is proposed to combine simulation and an ML metamodel that is trained incrementally using only selected data (smart data). A case study based on the sawmilling industry and experiments are shown, the results of which prove the possible advantages of this approach.

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Section: Data Access Scenariosmentioning

confidence: 99%

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Chabanet

El-Haouzi

Thomas

2021

Computers in Industry

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“…Extensive experiments on color images shows the feasibility of the model. Another interesting approach in this direction in 2019, has been suggested by Rastogi and Sharma [134] called as Fast Laplacian TSVM for Active Learning (F Lap − T W SV M AL ) where the authors proposed to identify the most informative and representative training points whose labels are queried for domain experts for annotations. Once the corresponding labels are acquired, this limited labeled and unlabeled data are used to train a fast model that involves solving a QPP and an unconstrained minimization problem to seek the classification hyperplanes.…”

Section: Twin Support Vector Machine For Multi-class Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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“…Yang et al [20] developed a Laplacian twin parametric-margin support vector machine (LTPSVM), and in 2018, they combined a safe screening rule with LTPSVM to handle large-scale data [21]. To further improve the training computational time, Reshma et al [22] proposed FLap-TSVM, which solves a smaller QPP and unconstrained minimization. Chen et al [23] introduced the Laplacian least squares TSVM (Lap-LSTSVM), which solves the two linear equation systems, reducing the computational cost.…”

Section: Introductionmentioning

confidence: 99%

Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification

2023

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Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM.INDEX TERMS Laplacian twin support vector machine, Pinball loss function, Semi-supervised classification.

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Fast Laplacian twin support vector machine with active learning for pattern classification

Cited by 13 publications

References 30 publications

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Comprehensive Review On Twin Support Vector Machines

Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification

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