NDLI: An Adaptable Time Warping Distance for Time Series Learning

Content Provider	IEEE Xplore Digital Library
Author	Gaudin, R. Nicoloyannis, N.
Copyright Year	2006
Description	Author affiliation: Lab. ERIC, Univ. Lumiere Lyon2 (Gaudin, R.; Nicoloyannis, N.)
Abstract	Most machine learning and data mining algorithms for time series datasets need a suitable distance measure. In addition to classic p-norm distance, numerous other distance measures exist and the most popular is dynamic time warping. Here we propose a new distance measure, called adaptable time warping (ATW), which generalizes all previous time warping distances. We present a learning process using a genetic algorithm that adapts ATW in a locally optimal way, according to the current classification issue we have to resolve. It's possible to prove that ATW with optimal parameters is at least equivalent or at best superior to the other time warping distances for all classification problems. We show this assertion by performing comparative tests on two real datasets. The originality of this work is that we propose a whole learning process directly based on the distance measure rather than on the time series themselves
Sponsorship	IEEE CPS
Starting Page	213
Ending Page	218
File Size	227387
Page Count	6
File Format	PDF
ISBN	0769527353
DOI	10.1109/ICMLA.2006.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Machine learning algorithms Measurement standards Machine learning Time measurement Data mining Association rules Genetic algorithms Testing Indexing
Content Type	Text
Resource Type	Article

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