Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Mattia Zanon, Gian Antonio Susto, Seán McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.
Original languageEnglish
Title of host publicationProceedings of the 19th IFAC World Congress, 2014
EditorsEdward Boje, Xiahou Xia
PublisherInternational Federation of Automatic Control
Pages1947-1952
Number of pages6
Volume19
Edition1
ISBN (Print)978-3-902823-62-5
DOIs
Publication statusPublished - Aug 2014
Event19th World Congress of the International Federation of Automatic Control (IFAC 2014) - Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014

Publication series

NameWorld Congress
PublisherInternational Federation of Automatic Control
Number1
Volume19
ISSN (Print)1474-6670

Conference

Conference19th World Congress of the International Federation of Automatic Control (IFAC 2014)
CountrySouth Africa
CityCape Town
Period24/08/201429/08/2014

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  • Cite this

    Zanon, M., Susto, G. A., & McLoone, S. (2014). Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach. In E. Boje, & X. Xia (Eds.), Proceedings of the 19th IFAC World Congress, 2014 (1 ed., Vol. 19, pp. 1947-1952). (World Congress; Vol. 19, No. 1). International Federation of Automatic Control. https://doi.org/10.3182/20140824-6-ZA-1003.01885