Unsupervised feature selection with ensemble learning
| LEADER | caa a22 4500 | ||
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| 001 | 605478058 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-013-5337-8 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-013-5337-8 | ||
| 245 | 0 | 0 | |a Unsupervised feature selection with ensemble learning |h [Elektronische Daten] |c [Haytham Elghazel, Alex Aussem] |
| 520 | 3 | |a In this paper, we show that the way internal estimates are used to measure variable importance in Random Forests are also applicable to feature selection in unsupervised learning. We propose a new method called Random Cluster Ensemble (RCE for short), that estimates the out-of-bag feature importance from an ensemble of partitions. Each partition is constructed using a different bootstrap sample and a random subset of the features. We provide empirical results on nineteen benchmark data sets indicating that RCE, boosted with a recursive feature elimination scheme (RFE) (Guyon and Elisseeff, Journal of Machine Learning Research, 3:1157-1182, 2003), can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art supervised and unsupervised algorithms, with a very limited subset of features. The method shows promise to deal with very large domains. All results, datasets and algorithms are available on line ( http://perso.univ-lyon1.fr/haytham.elghazel/RCE.zip ). | |
| 540 | |a The Author(s), 2013 | ||
| 690 | 7 | |a Unsupervised learning |2 nationallicence | |
| 690 | 7 | |a Feature selection |2 nationallicence | |
| 690 | 7 | |a Ensemble methods |2 nationallicence | |
| 690 | 7 | |a Random forest |2 nationallicence | |
| 700 | 1 | |a Elghazel |D Haytham |u University of Lyon, 69622, Lyon, France |4 aut | |
| 700 | 1 | |a Aussem |D Alex |u University of Lyon, 69622, Lyon, France |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 157-180 |x 0885-6125 |q 98:1-2<157 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-013-5337-8 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-013-5337-8 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Elghazel |D Haytham |u University of Lyon, 69622, Lyon, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Aussem |D Alex |u University of Lyon, 69622, Lyon, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 157-180 |x 0885-6125 |q 98:1-2<157 |1 2015 |2 98 |o 10994 | ||