Genetic Outlier Detection for a Robust Support Vector Machine

Lee, Heesung; Kim, Euntai

doi:10.5391/ijfis.2015.15.2.96

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…In this subsection, the support vector machine (SVM) [10] and multi-layer perceptron (MLP) [11] methods were employed to develop a risk model. They have been used in a variety of pattern classification, data mining, and data analysis applications.…”

Section: Risk Modelmentioning

confidence: 99%

“…According to this reference, ANN has been drastically employed since the early 1990s, such as constitute modelling, geo-material characterization, assessment of bearing capacity of pile, slope stability, evaluation of liquefaction, shallow foundations, and tunnels and underground openings. In this study, two ANN-based models, support vector machine (SVM) [10] and multi-layer perceptron (MLP) [11], were employed to establish the risk model with respect to the underground utilities (mainly water supply and sewer pipe systems) along the railway network.…”

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confidence: 99%

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Establishing an ANN-Based Risk Model for Ground Subsidence Along Railways

Lee

2018

Applied Sciences

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Ground subsidence occurrences have drastically increased in the Seoul area of the Republic of Korea. The structural defects of underground utilities were found to be the primary cause of ground subsidence based on several field investigations. This paper presents a risk model that assesses the probability of occurrence of ground subsidence along railways. In this study, support vector machine (SVM) and multi-layer perceptron (MLP) approaches were successfully employed to develop an artificial neural network (ANN)-based risk model. The risk model, in conjunction with a database composed of underground utilities and geological boring data along urban railway networks, was utilized to develop a hazard map system. A limited field experimental program was conducted for the purpose of verification, resulting in a promising tool to effectively maintain railway networks.

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Section: Risk Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Establishing an ANN-Based Risk Model for Ground Subsidence Along Railways

Lee

2018

Applied Sciences

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“…In other words, after the division of an entire group into subgroups, a decision function is selected; this function can minimize the empirical rick for the subgroups. Thus, the SVM method has the advantage of achieving great performance in classification, prediction, and estimation processes by using a relatively low amount of the given learning data [22][23][24][25][26][27].…”

Section: Support Vector Machinementioning

confidence: 99%

“…In this paper, we propose a classification method that uses a support vector machine (SVM) to separate the anomalous propagation echoes. The SVM method derives the best hyperspace that divides the given data into two groups and is selected because of its following advantages: great performance in practical applications such as artificial neural network, rapid classification using relatively less data, problem compensations such as over fitting, and local optimization search [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Anomalous Propagation Echo Classification of Imbalanced Radar Data with Support Vector Machine

Lee

Kim

2016

Advances in Meteorology

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A number of technologically advanced devices, such as radars and satellites, are used in an actual weather forecasting process. Among these devices, the radar is essential equipment in this process because it has a wide observation area and fine resolution in both the time and the space domains. However, the radar can also observe unwanted nonweather phenomena. Anomalous propagation echo is one of the representative nonprecipitation echoes generated by an abnormal refraction phenomenon of a radar beam. Abnormal refraction occurs when the temperature and the humidity change dramatically. In such a case, the radar recognizes either the ground or the sea surface as an atmospheric object. This false observation decreases the accuracy of both quantitative precipitation estimation and weather forecasting. Therefore, a system that can automatically recognize an anomalous propagation echo from the radar data needs to be developed. In this paper, we propose a classification method for separating anomalous propagation echoes from the rest of the weather data by using a combination of a support vector machine classifier and the synthetic minority oversampling technique, to solve the problem of imbalanced data. By using actual cases of anomalous propagation we have confirmed that the proposed method provides good classification results.

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“…GAs have been used for many scientific and engineering optimization and search problems [4]. GAs provide an adaptive and robust computational procedure modeled on the mechanics of natural genetic systems [5]. GAs can be considered as a good solution for finding a richer set of alternatives beyond PCA, LDA and LPP in search space, because they are complicated problem in a large search space [6].…”

Section: Introductionmentioning

confidence: 99%

Combining Locality Preserving Projection with Global Information for Efficient Recognition

Lee¹

2018

IJFIS

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This paper proposes a new feature extraction scheme, combining global and local features. The proposed method uses principal component analysis (PCA) and linear discriminant analysis (LDA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA and LDA are known for extracting the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. The proposed combing method integrates global and local descriptive information and finds an efficient set of alternatives beyond PCA, LDA and LPP in the parametric space. Further, In order to find optimal parameters, the genetic algorithm (GA) is employed. Experiments are performed with four data sets selected in UCI machine learning repository to show the performance of the proposed algorithm.

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Genetic Outlier Detection for a Robust Support Vector Machine

Cited by 11 publications

References 15 publications

Establishing an ANN-Based Risk Model for Ground Subsidence Along Railways

Establishing an ANN-Based Risk Model for Ground Subsidence Along Railways

Anomalous Propagation Echo Classification of Imbalanced Radar Data with Support Vector Machine

Combining Locality Preserving Projection with Global Information for Efficient Recognition

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