Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A combination of variable selection and data mining techniques for high-dimensional statistical modelling

A combination of variable selection and data mining techniques for high-dimensional statistical... Variable selection is fundamental to statistical modelling in diverse fields of sciences. This paper deals with the problem of high-dimensional statistical modelling through the analysis of seismological data in Greece acquired during the years 1962–2003. The dataset consists of 10,333 observations and 11 factors, used to detect possible risk factors of large earthquakes. In our study, different statistical variable selection techniques are applied, while data mining techniques enable us to discover associations, meaningful patterns and rules. The statistical methods employed in this work were the non-concave penalised likelihood methods, SCAD, LASSO and Hard, the generalised linear logistic regression and the best subset variable selection. The applied data mining methods were three decision trees algorithms, the classification and regression tree (C&RT), the chi-square automatic interaction detection (CHAID) and the C5.0 algorithm. The way of identifying the significant variables in large datasets along with the performance of used techniques are also discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

A combination of variable selection and data mining techniques for high-dimensional statistical modelling

Loading next page...
 
/lp/inderscience-publishers/a-combination-of-variable-selection-and-data-mining-techniques-for-TOmJS2tGj0
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2013.053799
Publisher site
See Article on Publisher Site

Abstract

Variable selection is fundamental to statistical modelling in diverse fields of sciences. This paper deals with the problem of high-dimensional statistical modelling through the analysis of seismological data in Greece acquired during the years 1962–2003. The dataset consists of 10,333 observations and 11 factors, used to detect possible risk factors of large earthquakes. In our study, different statistical variable selection techniques are applied, while data mining techniques enable us to discover associations, meaningful patterns and rules. The statistical methods employed in this work were the non-concave penalised likelihood methods, SCAD, LASSO and Hard, the generalised linear logistic regression and the best subset variable selection. The applied data mining methods were three decision trees algorithms, the classification and regression tree (C&RT), the chi-square automatic interaction detection (CHAID) and the C5.0 algorithm. The way of identifying the significant variables in large datasets along with the performance of used techniques are also discussed.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2013

There are no references for this article.