BOOSTING INTERVAL-BASED LITERALS: VARIABLE LENGTH AND EARLY CLASSIFICATION - Data Mining in Time Series Databases - page 149

CHAPTER 7
BOOSTING INTERVAL-BASED LITERALS: VARIABLE
LENGTH AND EARLY CLASSIFICATION ∗
Carlos J. Alonso Gonzalez
Dpto. de Informatica, Universidad de Valladolid, Spain
Grupo de Sistemas Inteligentes
E-mail: [e-mail]
Juan J. Rodrıguez Diez
Lenguajes y Sistemas Informaticos, Universidad de Burgos, Spain
Grupo de Sistemas Inteligentes
E-mail: [e-mail]
This work presents a system for supervised time series classification,
capable of learning from series of different length and able of providing
a classification when only part of the series are presented to the clas-
sifier. The induced classifiers consist of a linear combination of literals,
obtained by boosting base classifiers that contain only one literal. Never-
theless, these literals are specifically designed for the task at hand and
they test properties of fragments of the time series on temporal inter-
vals. The method had already been developed for fixed length time series.
This work exploits the symbolic nature of the classifier to add it two new
features. First, the system has been slightly modified in order that it is
now able to learn directly from variable length time series. Second, the
classifier can be used to identify partial time series. This “early classi-
fication” is essential in some task, like on line supervision or diagnosis,
where it is necessary to give an alarm signal as soon as possible. Several
experiments on different data test are presented, which illustrate that
the proposed method is highly competitive with previous approaches in
terms of classification accuracy.
Keywords : Interval based literal; boosting; time series classification;
machine learning.
∗ This work has been supported by the Spanish CYCIT project TAP 99-0344 and the
“Junta de Castilla y Leon” project VA101/01.
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