Sequential Heterogeneous Feature Selection for Multi-class Classification: Application in Government 2.0

Herein, the problem of multi-class classification of participatory civil issue reports in crowdsourcing platforms is addressed. Specifically, an efficient method is proposed to guide the selection of heterogeneous features, so as to account for different information facets of the reported issue. An optimization framework is devised to select the minimum number of informative features from each feature set, and switch between feature sets when deemed necessary to achieve an accurate classification decision. Evaluation on real-world data from SeeClickFix, a government 2.0 platform, shows the ability of the proposed framework to classify civil issue reports with up to 92.6% accuracy, while using 99.82% less features than the state-of-the-art.