TY - GEN
T1 - Wisdom of crowds: An empirical study of ensemble-based feature selection strategies
AU - Susnjak, Teo
AU - Kerry, David
AU - Barczak, Andre
AU - Reyes, Napoleon
AU - Gal, Yaniv
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - The accuracy of feature selection methods is affected by both the nature of the underlying datasets and the actual machine learning algorithms they are combined with. The role these factors have in the final accuracy of the classifiers is generally unknown in advance. This paper presents an ensemble-based feature selection approach that addresses this uncertainty and mitigates against the variability in the generalisation of the classifiers. The study conducts extensive experiments with combinations of three feature selection methods on nine datasets, which are trained on eight different types of machine learning algorithms. The results confirm that the ensemble based approaches to feature selection tend to produce classifiers with higher accuracies, are more reliable due to decreased variances and are thus more generalisable.
AB - The accuracy of feature selection methods is affected by both the nature of the underlying datasets and the actual machine learning algorithms they are combined with. The role these factors have in the final accuracy of the classifiers is generally unknown in advance. This paper presents an ensemble-based feature selection approach that addresses this uncertainty and mitigates against the variability in the generalisation of the classifiers. The study conducts extensive experiments with combinations of three feature selection methods on nine datasets, which are trained on eight different types of machine learning algorithms. The results confirm that the ensemble based approaches to feature selection tend to produce classifiers with higher accuracies, are more reliable due to decreased variances and are thus more generalisable.
UR - http://www.scopus.com/inward/record.url?scp=84952684852&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26350-2_47
DO - 10.1007/978-3-319-26350-2_47
M3 - Conference contribution
AN - SCOPUS:84952684852
SN - 9783319263496
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 526
EP - 538
BT - AI 2015
A2 - Renz, Jochen
A2 - Pfahringer, Bernhard
PB - Springer-Verlag London Ltd.
T2 - 28th Australasian Joint Conference on Artificial Intelligence, AI 2015
Y2 - 30 November 2015 through 4 December 2015
ER -