Support Vector Machines: A Review and Applications in Statistical Process Monitoring

Apsemidis, Anastasios; Psarakis, Stelios

doi:10.1002/9781119721871.ch7

Cited by 4 publications

(4 citation statements)

References 68 publications

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“…21 The most popular way SVMs are implemented into SPM is to solve the problems of pattern recognition; see Apsemidis and Psarakis. 17 In SPM, there are different types of patterns that can be present in the data. Some of these patterns include: an upward or downward trend, an upward or downward shift, or a cyclical pattern.…”

Section: Support Vector Machine In Spmmentioning

confidence: 99%

“…The most popular way SVMs are implemented into SPM is to solve the problems of pattern recognition; see Apsemidis and Psarakis 17 . In SPM, there are different types of patterns that can be present in the data.…”

Section: Enhancement Of the Mehwma Scheme Using A Support Vector Machinementioning

confidence: 99%

“…Some of the ML techniques used recently in SPM include artificial neural networks (ANN), multivariate adaptive regression splines (MARS), and support vector machines (SVMs); see Chowdhury and Janan 15 and Yeganeh et al 16 . Based on the works by Apsemidis and Psarakis 17 as well as, Chowdhury and Janan, 15 SVMs have been shown to be useful in SPM as they provide good results even when compared to other ML techniques such as the ANN, and thus SVMs will be used and integrated with the new monitoring scheme.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

Pieters

Malela‐Majika

Human

et al. 2023

Quality & Reliability Eng

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Statistical process monitoring (SPM) provides a way, by means of monitoring schemes, to be alerted when a process significantly changes, and to initiate investigations for potential causes of variation. However, these schemes can only determine if the process is stable or unstable and they cannot provide any additional information. Several researchers have recommended the integration of machine learning (ML) approaches into SPM as a solution for the shortfalls of the techniques used to construct traditional monitoring schemes. This paper introduces a new multivariate extended homogeneously weighted moving average (MEHWMA) monitoring scheme and investigates its performance in terms of the run‐length distribution using simulation. In addition, the proposed MEHWMA scheme is integrated with a support vector machine to allow for the classification of the out‐of‐control events which facilitates the identification of the root causes of variation in the process. A numerical illustrative example is provided using real‐life data.

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Section: Support Vector Machine In Spmmentioning

confidence: 99%

Section: Enhancement Of the Mehwma Scheme Using A Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

Pieters

Malela‐Majika

Human

et al. 2023

Quality & Reliability Eng

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show abstract

“…Therefore, are transformed into linearly separable points. Therefore, data points that belong to different classes have clearer separation boundaries [49].…”

Section: B Support Vector Machinementioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.

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Advantages of Oversampling Techniques: A Case Study in Risk Factors for Fall Prediction

Sihag

Yadav

Vijay

et al. 2023

Communications in Computer and Information Science

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Support Vector Machines: A Review and Applications in Statistical Process Monitoring

Cited by 4 publications

References 68 publications

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

A new multivariate extended homogeneously weighted moving average monitoring scheme incorporated with a support vector machine

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Advantages of Oversampling Techniques: A Case Study in Risk Factors for Fall Prediction

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