SWITCH: A novel approach to ensemble learning for heterogeneous data

Rong Jin, Huan Liu

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

1 Citation (Scopus)

Abstract

The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capture the diverse aspects of heterogeneous data. If an ensemble can learn the relationship between different portions of data and their corresponding models, the ensemble can selectively apply models to unseen data according to the learned relationship. We propose a novel approach to enable the learning of the relationships between data and models by creating a set of 'switches' that can route a testing instance to appropriate classification models in an ensemble. Our empirical study on both real-world data and benchmark data shows that the proposed approach to ensemble learning can achieve significant performance improvement for heterogeneous data.

Original languageEnglish (US)
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsJ.-F. Boulicaut, F. Esposito, D. Pedreschi, F. Giannotti
Pages560-562
Number of pages3
Volume3201
StatePublished - 2004
Event15th European Conference on Machine Learning, ECML 2004 - Pisa, Italy
Duration: Sep 20 2004Sep 24 2004

Other

Other15th European Conference on Machine Learning, ECML 2004
CountryItaly
CityPisa
Period9/20/049/24/04

Fingerprint

Image classification
Learning systems
Switches
Testing

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Jin, R., & Liu, H. (2004). SWITCH: A novel approach to ensemble learning for heterogeneous data. In J-F. Boulicaut, F. Esposito, D. Pedreschi, & F. Giannotti (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 560-562)

SWITCH : A novel approach to ensemble learning for heterogeneous data. / Jin, Rong; Liu, Huan.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / J.-F. Boulicaut; F. Esposito; D. Pedreschi; F. Giannotti. Vol. 3201 2004. p. 560-562.

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

Jin, R & Liu, H 2004, SWITCH: A novel approach to ensemble learning for heterogeneous data. in J-F Boulicaut, F Esposito, D Pedreschi & F Giannotti (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 3201, pp. 560-562, 15th European Conference on Machine Learning, ECML 2004, Pisa, Italy, 9/20/04.
Jin R, Liu H. SWITCH: A novel approach to ensemble learning for heterogeneous data. In Boulicaut J-F, Esposito F, Pedreschi D, Giannotti F, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 3201. 2004. p. 560-562
Jin, Rong ; Liu, Huan. / SWITCH : A novel approach to ensemble learning for heterogeneous data. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / J.-F. Boulicaut ; F. Esposito ; D. Pedreschi ; F. Giannotti. Vol. 3201 2004. pp. 560-562
@inproceedings{e20361cbbc01478291748a1a1ea4c5f1,
title = "SWITCH: A novel approach to ensemble learning for heterogeneous data",
abstract = "The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capture the diverse aspects of heterogeneous data. If an ensemble can learn the relationship between different portions of data and their corresponding models, the ensemble can selectively apply models to unseen data according to the learned relationship. We propose a novel approach to enable the learning of the relationships between data and models by creating a set of 'switches' that can route a testing instance to appropriate classification models in an ensemble. Our empirical study on both real-world data and benchmark data shows that the proposed approach to ensemble learning can achieve significant performance improvement for heterogeneous data.",
author = "Rong Jin and Huan Liu",
year = "2004",
language = "English (US)",
volume = "3201",
pages = "560--562",
editor = "J.-F. Boulicaut and F. Esposito and D. Pedreschi and F. Giannotti",
booktitle = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

}

TY - GEN

T1 - SWITCH

T2 - A novel approach to ensemble learning for heterogeneous data

AU - Jin, Rong

AU - Liu, Huan

PY - 2004

Y1 - 2004

N2 - The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capture the diverse aspects of heterogeneous data. If an ensemble can learn the relationship between different portions of data and their corresponding models, the ensemble can selectively apply models to unseen data according to the learned relationship. We propose a novel approach to enable the learning of the relationships between data and models by creating a set of 'switches' that can route a testing instance to appropriate classification models in an ensemble. Our empirical study on both real-world data and benchmark data shows that the proposed approach to ensemble learning can achieve significant performance improvement for heterogeneous data.

AB - The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image classification, the training data are often collected from multiple sources and heterogeneous. Ensemble learning is a proven effective approach to heterogeneous data, which uses multiple classification models to capture the diverse aspects of heterogeneous data. If an ensemble can learn the relationship between different portions of data and their corresponding models, the ensemble can selectively apply models to unseen data according to the learned relationship. We propose a novel approach to enable the learning of the relationships between data and models by creating a set of 'switches' that can route a testing instance to appropriate classification models in an ensemble. Our empirical study on both real-world data and benchmark data shows that the proposed approach to ensemble learning can achieve significant performance improvement for heterogeneous data.

UR - http://www.scopus.com/inward/record.url?scp=22944435849&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=22944435849&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:22944435849

VL - 3201

SP - 560

EP - 562

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

A2 - Boulicaut, J.-F.

A2 - Esposito, F.

A2 - Pedreschi, D.

A2 - Giannotti, F.

ER -