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Compressive sensing-enhanced feature selection and its application in travel mode choice prediction

Journal Article


Abstract


  • Travel mode choice (TMC) prediction aims to identify the potential travel means for individual commuter. The difficulty associated with the TMC prediction is how to make full use of a large number of available factors (or features), and balance the trade-off between the number of selected features and yielded prediction performance. This paper presents a novel feature selection algorithm based on the compressive sensing model, in which candidate features are arranged as a basic dictionary. Features are later ranked and selected based on their contribution to the travel mode. The advantage of the proposed algorithm is two-fold: it is able to select important features that minimize the prediction error; and the feature selection process depends less on the priori domain knowledge. The generality of the proposed algorithm is evaluated using several benchmark classification problems and a real-world household travel survey data. Experimental results demonstrates that the proposed algorithm outperforms state-of-the-art methods via selecting less number of features and achieving the satisfactory classification performance simultaneously.

Publication Date


  • 2019

Citation


  • Yang, J. & Ma, J. (2019). Compressive sensing-enhanced feature selection and its application in travel mode choice prediction. Applied Soft Computing Journal, 75 537-547.

Scopus Eid


  • 2-s2.0-85057621787

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/252

Number Of Pages


  • 10

Start Page


  • 537

End Page


  • 547

Volume


  • 75

Place Of Publication


  • Netherlands

Abstract


  • Travel mode choice (TMC) prediction aims to identify the potential travel means for individual commuter. The difficulty associated with the TMC prediction is how to make full use of a large number of available factors (or features), and balance the trade-off between the number of selected features and yielded prediction performance. This paper presents a novel feature selection algorithm based on the compressive sensing model, in which candidate features are arranged as a basic dictionary. Features are later ranked and selected based on their contribution to the travel mode. The advantage of the proposed algorithm is two-fold: it is able to select important features that minimize the prediction error; and the feature selection process depends less on the priori domain knowledge. The generality of the proposed algorithm is evaluated using several benchmark classification problems and a real-world household travel survey data. Experimental results demonstrates that the proposed algorithm outperforms state-of-the-art methods via selecting less number of features and achieving the satisfactory classification performance simultaneously.

Publication Date


  • 2019

Citation


  • Yang, J. & Ma, J. (2019). Compressive sensing-enhanced feature selection and its application in travel mode choice prediction. Applied Soft Computing Journal, 75 537-547.

Scopus Eid


  • 2-s2.0-85057621787

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/252

Number Of Pages


  • 10

Start Page


  • 537

End Page


  • 547

Volume


  • 75

Place Of Publication


  • Netherlands