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