High‐dimensional data classification model based on random projection and Bagging‐ support vector machine

2020 ◽  
Author(s):  
Yujia Sun ◽  
Jan Platoš
Keyword(s):  
Support Vector Machine ◽  
High Dimensional Data ◽  
Data Classification ◽  
Random Projection ◽  
Classification Model ◽  
High Dimensional ◽  
Support Vector ◽  
Model Based

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.

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors
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