Support Vector Machines

Guenther, Nick; Schonlau, Matthias

doi:10.1177/1536867x1601600407

Cited by 184 publications

(108 citation statements)

References 16 publications

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“…Because AdaBoost is an integration of single classifiers, the typical single classifiers are set as the contrast, including the support vector machine (SVM) [37] and decision tree (DT) [38]. The same training set of 180 samples is used for the classifier construction.…”

Section: Comparison Of Expert Classificationmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

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Algal bloom is a typical pollution of urban lakes, which threatens drinking safety and breaks the urban landscape. It is pivotal to select a reasonable governance approach for sustainable management. A decision-making support method was studied in this paper. First, a general framework was designed to organize the rational decision-making processes. Second, quantitative calculation methods were proposed, including expert selection and opinion integration. The methods can determine the vital decision elements objectively and automatically. Third, the method was applied in Yuyuantan Lake in Beijing, China. The monitoring information and decision-making process are presented and the rank of governance alternatives is given. The comparison and discussion show that the group decision-making method is feasible and effective. It can assist the sustainable management of algal bloom.

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Section: Comparison Of Expert Classificationmentioning

confidence: 99%

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

et al. 2020

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“…1) Support Vector Machine: SVM is an algorithm that classifies data by constructing a set of hyper-planes in high dimensions [18]. To simplify the computations, kernel functions are used to represent the mapping of the data.…”

Section: B Classification Algorithmsmentioning

confidence: 99%

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

Sakhnini

Karimipour

2019

2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE)

102

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False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.

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“…Choosing a good classifier is an important task to build a robust state-of-the-art predictive ability. In this work various machine learning algorithms; Support Vector Machine (SVM) [43], naïve Bayes' [44], decision tree [45], random forest [46], KNN [47] and ensemble classification methods (with bagging and boosting) [48], etc. are considered.…”

Section: Classification Algorithmmentioning

confidence: 99%

An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

et al. 2019

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The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.

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Support Vector Machines

Cited by 184 publications

References 16 publications

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

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