Boosting unsupervised competitive learning ensembles

Emilio Corchado; Bruno Baruque; Hujun Yin

Boosting unsupervised competitive learning ensembles

Emilio Corchado, Bruno Baruque, Hujun Yin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Publisher	Springer Nature
Pages	339-348
Number of pages	9
Volume	4668
ISBN (Print)	9783540746898
Publication status	Published - 2007
Event	17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto Duration: 1 Jul 2007 → …

Conference

Conference	17th International Conference on Artificial Neural Networks, ICANN 2007
City	Porto
Period	1/07/07 → …

Keywords

Bagging
Boosting
Topology preserving mappings
Unsupervised learning

Cite this

@inproceedings{3d856e5fde0a48b9aec3c5680c5d5326,

title = "Boosting unsupervised competitive learning ensembles",

abstract = "Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

keywords = "Bagging, Boosting, Topology preserving mappings, Unsupervised learning",

author = "Emilio Corchado and Bruno Baruque and Hujun Yin",

year = "2007",

language = "English",

isbn = "9783540746898",

volume = "4668",

pages = "339--348",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.",

publisher = "Springer Nature",

address = "United States",

note = "17th International Conference on Artificial Neural Networks, ICANN 2007 ; Conference date: 01-07-2007",

}

Corchado, E, Baruque, B & Yin, H 2007, Boosting unsupervised competitive learning ensembles. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 4668, Springer Nature, pp. 339-348, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, 1/07/07.

Boosting unsupervised competitive learning ensembles. / Corchado, Emilio; Baruque, Bruno; Yin, Hujun.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 4668 Springer Nature, 2007. p. 339-348.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Boosting unsupervised competitive learning ensembles

AU - Corchado, Emilio

AU - Baruque, Bruno

AU - Yin, Hujun

PY - 2007

Y1 - 2007

N2 - Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM. © Springer-Verlag Berlin Heidelberg 2007.

AB - Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM. © Springer-Verlag Berlin Heidelberg 2007.

KW - Bagging

KW - Boosting

KW - Topology preserving mappings

KW - Unsupervised learning

M3 - Conference contribution

SN - 9783540746898

VL - 4668

SP - 339

EP - 348

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

PB - Springer Nature

T2 - 17th International Conference on Artificial Neural Networks, ICANN 2007

Y2 - 1 July 2007

ER -

Boosting unsupervised competitive learning ensembles

Abstract

Conference

Keywords

Fingerprint

Cite this