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Feature Selection for Change Detection in Multivariate Time-Series

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2 Author(s)
Michael Botsch ; Institute for Circuit Theory and Signal Processing, Technical University Munich, 80333 Munich, Germany. Email: botsch@tum.de ; Josef A. Nossek

In machine learning the preprocessing of the observations and the resulting features are one of the most important factors for the performance of the final system. In this paper a method to perform feature selection for change detection in multivariate time-series is presented. Feature selection aims to determine a small subset which is representative for the change detection task from a given set of features. We are dealing with time-series where the classification has to be done on time-stamp level, although the smallest independent entity is a scenario consisting of one or more time-series. Despite this difficulty we will show how feature selection based on the generalization ability of a classifier can be realized by defining a cost function on scenario level. For the classification step in the feature selection process a modified random forest (RF) algorithm - which we will call scenario based random forest (SBRF) - is used due to its intrinsic possibility to estimate the generalization error. The excellent performance of the proposed feature selection algorithm will be shown in a car crash detection application

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007