Detection of outliers in high-dimensional data using nu-support vector regression

2021 ◽  
pp. 1-20
Author(s):  
Abdullah Mohammed Rashid ◽  
Habshah Midi ◽  
Waleed Dhhan ◽  
Jayanthi Arasan
Keyword(s):  
Support Vector Regression ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Support Vector ◽  
Detection Of Outliers

scholarly journals Predicting the biomechanical strength of proximal femur specimens through high dimensional geometric features and support vector regression

2013 ◽  
Author(s):  
Chien-Chun Yang ◽  
Mahesh B. Nagarajan ◽  
Markus B. Huber ◽  
Julio Carballido-Gamio ◽  
Jan S. Bauer ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Proximal Femur ◽  
High Dimensional ◽  
Support Vector ◽  
Geometric Features ◽  
Biomechanical Strength

A bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique for high-dimensional problems

2016 ◽  
Vol 231 (12) ◽  
pp. 2173-2186 ◽  
Author(s):  
Yaping Ju ◽  
Geoff Parks ◽  
Chuhua Zhang
Keyword(s):  
Support Vector Regression ◽  
Computational Cost ◽  
Model Representation ◽  
High Dimensional ◽  
Support Vector ◽  
High Dimensional Model Representation ◽  
Dimensional Model ◽  
Comparison Results ◽  
Modeling Accuracy ◽  
Metamodeling Technique

A major challenge of metamodeling in simulation-based engineering design optimization is to handle the “curse of dimensionality,” i.e. the exponential growth of computational cost with increase of problem dimensionality. Encouragingly, it has been reported recently that a high-dimensional model representation assisted by a radial basis function is capable of deriving high-dimensional input–output relationships at dramatically reduced computational cost. In this article, support vector regression is employed as an alternative to be coupled with high-dimensional model representation for the metamodeling of high-dimensional problems. In particular, the bisection sampling method is proposed to be used in the metamodeling process to generate high-quality training samples. Testing and comparison results show that the developed bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique can achieve high modeling accuracy with a smaller number of training sample evaluations. For the problem examined in this study, the bisection-sampling-based support vector regression–high-dimensional model representation enables high modeling accuracy and linear computational complexity as the problem dimensionality increases. Analysis of this performance advantage shows that the use of bisection method enables the developed metamodeling technique to be more effective in dealing with high-dimensional problems.

scholarly journals High-Dimensional Data Classification Based on Smooth Support Vector Machines

2015 ◽  
Vol 72 ◽  
pp. 477-484 ◽  
Author(s):  
Santi Wulan Purnami ◽  
Shofi Andari ◽  
Yuniati Dian Pertiwi
Keyword(s):  
Support Vector Machines ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional ◽  
Support Vector ◽  
Vector Machines

scholarly journals Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2840 ◽  
Author(s):  
Lawrence Muriira ◽  
Zhiwei Zhao ◽  
Geyong Min
Keyword(s):  
Support Vector Machine ◽  
Sensor Networks ◽  
High Dimensional Data ◽  
Data Classification ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Moderate Degree ◽  
Linear Support Vector Machine ◽  
Link Classification

Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.

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