Feature Selection for Change Detection in Multivariate Time-Series
Michael Botsch, Josef A. Nossek · 2007
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