Unbalanced Decision Trees for Multi-class Classification


Ramanan, Amirthalingam, Suppharangsan, Somjet and Niranjan, Mahesan (2007) Unbalanced Decision Trees for Multi-class Classification At IEEE - Second International Conference on Industrial and Information Systems, ICIIS 2007, Sri Lanka. 08 - 11 Aug 2007. , pp. 291-294.

Download

[img] PDF 188.pdf - Version of Record
Download (306kB)

Description/Abstract

In this paper we propose a new learning architecture that we call Unbalanced Decision Tree (UDT), attempting to improve existing methods based on Directed Acyclic Graph (DAG) and One-versus-All (OVA) approaches to multi-class pattern classification tasks. Several standard techniques, namely One-versus-One (OVO), OVA, and DAG, are compared against UDT by some benchmark datasets from the University of California, Irvine (UCI) repository of machine learning databases. Our experiments indicate that UDT is faster in testing compared to DAG, while maintaining accuracy comparable to those standard algorithms tested. This new learning architecture UDT is general, and could be applied to any classification task in machine learning in which there are natural groupings among the patterns.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: 8-11 August 2007
Venue - Dates: IEEE - Second International Conference on Industrial and Information Systems, ICIIS 2007, Sri Lanka, 2007-08-08 - 2007-08-11
Organisations: Southampton Wireless Group
ePrint ID: 271490
Date :
Date Event
10 August 2007Published
Date Deposited: 18 Aug 2010 18:46
Last Modified: 17 Apr 2017 18:14
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/271490

Actions (login required)

View Item View Item