Context-sensitive regression analysis for distributed data

James Duggan

Context-sensitive regression analysis for distributed data

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

A precondition of existing ensemble-based distributed data mining techniques is the assumption that contributing data are identically and independently distributed. However, this assumption is not valid in many virtual organization contexts because contextual heterogeneity exists. Focusing on regression tasks, this paper proposes a context-based meta-learning technique for horizontally partitioned data with contextual heterogeneity. The predictive performance of our new approach and the state of the art techniques are evaluated and compared on both simulated and real-world data sets.

Original language	English (Ireland)
Title of host publication	ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS
Publisher	SPRINGER-VERLAG BERLIN
Number of pages	7
Volume	3584
ISBN (Electronic)	0302-9743
ISBN (Print)	0302-9743
Publication status	Published - 1 Jan 2005

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Xing, Y;Madden, MG;Duggan, J;Lyons, GJ

Cite this

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title = "Context-sensitive regression analysis for distributed data",

abstract = "A precondition of existing ensemble-based distributed data mining techniques is the assumption that contributing data are identically and independently distributed. However, this assumption is not valid in many virtual organization contexts because contextual heterogeneity exists. Focusing on regression tasks, this paper proposes a context-based meta-learning technique for horizontally partitioned data with contextual heterogeneity. The predictive performance of our new approach and the state of the art techniques are evaluated and compared on both simulated and real-world data sets.",

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AB - A precondition of existing ensemble-based distributed data mining techniques is the assumption that contributing data are identically and independently distributed. However, this assumption is not valid in many virtual organization contexts because contextual heterogeneity exists. Focusing on regression tasks, this paper proposes a context-based meta-learning technique for horizontally partitioned data with contextual heterogeneity. The predictive performance of our new approach and the state of the art techniques are evaluated and compared on both simulated and real-world data sets.

M3 - Conference Publication

SN - 0302-9743

VL - 3584

BT - ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS

PB - SPRINGER-VERLAG BERLIN

ER -

Context-sensitive regression analysis for distributed data

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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