Improving the predictive ability of multivariate calibration models using support vector data description

Walid Gani

International Journal of Data Analysis Techniques and Strategies, 2021, vol. 13, issue 3, 227-243

Abstract: Outlier detection is a crucial step in building multivariate calibration models and enhancing their predictive ability. However, traditional outlier detection methods often suffer from important drawbacks mainly their reliance on assumptions about the data model distribution and their unsuitability for real-life applications. This paper investigates the use of support vector data description (SVDD) for the detection of outliers and proposes a multivariate calibration strategy that combines partial least squares (PLS) and SVDD. For the assessment of the proposed calibration strategy, an experimental study aiming to predict four properties of diesel fuel is conducted. The results show that the predictive ability of PLS-SVDD is better than the predictive ability of a classical strategy that combines PLS and the T² method.

Keywords: multivariate; calibration?; outlier?; support; vector; data; description?; SVDD?; partial; least; squares?; PLS?; T; 2; method. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=118021 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:13:y:2021:i:3:p:227-243

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().