Monitoring network optimisation for spatial data classification using support vector machines. (7th November 2006)

• Record Type: Journal Article
• Authors: Pozdnoukhov, Alexei; Kanevski, Mikhail
• Is Part Of: International journal of environment and pollution. Volume 28:Number 3/4(2006)
• Page Start: 465
• Page End: 484
• Publication Date: 2006-11-07
• Subjects: monitoring network optimisation -- machine learning -- support vector machines -- active learning -- geostatistics -- spatial data classification -- climate data -- environmental pollution -- indicator kriging; Environmental policy -- Periodicals; Environmental engineering -- Periodicals; Environmental sciences -- Periodicals; Pollution -- Periodicals; 363.73
• Languages: English
• ISSNs: 0957-4352

Abstract

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.