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Redundant feature selection for telemetry data

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Taylor, P., Griffiths, Nathan, Bhalerao, Abhir, Popham, T. J., Zhou, X. and Dunoyer, A. (2014) Redundant feature selection for telemetry data. In: Cao , L. and Zeng, Y. and Symeonidis, A. L. and Gorodetsky, V. and Müller, J. P., (eds.) Agents and Data Mining Interaction. Lecture Notes in Computer Science, Volume 8316 . Heidelberg: Springer, pp. 53-65. ISBN 9783642551925

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Abstract

Feature sets in many domains often contain many irrelevant and redundant features, both of which have a negative effect on the performance and complexity of agents that use the data [9]. Supervised feature selection aims to overcome this problem by selecting features that are highly related to the class labels, yet unrelated to each other. One proposed technique to select good features with few inter-dependencies is minimal Redundancy Maximal Relevance (mRMR) [12], but this can be impractical with large feature sets. In many situations, features are extracted from signal data such as vehicle telemetry, medical sensors, or financial time-series, and it is possible for feature redundancies to exist both between features extracted from the same signal (intra-signal), and between features extracted from different signals (inter-signal). We propose a two stage selection process to take advantage of these different types of redundancy, considering intra-signal and inter-signal redundancies separately. We illustrate the process on vehicle telemetry signal data collected in a driver distraction monitoring project. We evaluate it using several machine learning algorithms: Random Forest; Naïve Bayes; and C4.5 Decision Tree. Our results show that this two stage process significantly reduces the computation required because of inter-dependency calculations, while having minimal detrimental effect on the performance of the feature sets produced.

Item Type: Book Item
Divisions: Faculty of Science > Computer Science
Series Name: Lecture Notes in Computer Science
Journal or Publication Title: Agents and Data Mining Interaction
Publisher: Springer
Place of Publication: Heidelberg
ISBN: 9783642551925
Book Title: Agents and Data Mining Interaction
Editor: Cao , L. and Zeng, Y. and Symeonidis, A. L. and Gorodetsky, V. and Müller, J. P.
Official Date: 1 May 2014
Dates:
DateEvent
1 May 2014Published
Volume: Volume 8316
Number of Pages: 137
Page Range: pp. 53-65
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Conference Paper Type: Paper
Title of Event: ADMI 2013 : 9th International Workshop Agents and Data Mining Interaction
Type of Event: Workshop
Location of Event: Saint Paul, MN, USA
Date(s) of Event: 6-7 May 2013
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