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Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framework to perform joint feature selection and classification on-the-fly while relaxing the assumption on feature independence. The effectiveness of the proposed approach is showed by classifying urban issue reports on the SeeClickFix civic engagement platform. A significant reduction in the average number of features used is observed without a drop in the classification accuracy. 

In this paper, the automatic classification of non-emergency civil issues in crowdsourcing systems is addressed in the case where multiple feature sets are available. We recognize that multiple feature sets can contain useful complementary information regarding the type of an issue leading to a more accurate decision. However, using all features in these sets may delay the decision. Since we are interested in reaching an accurate decision in a timely manner, an optimal way of selecting features from multiple feature sets is needed. To this end, we propose a novel approach that sequentially reviews available features and feature sets to decide whether the feature review process must continue in the current set or move to the next one. In the end, when all feature sets have been reviewed, the issue is classified using all available information. It is shown that the proposed approach is guaranteed to review the least number of features in all feature sets before reaching a decision, while the optimum decision rule is shown to minimize the average Bayes risk. Evaluation on real world SeeClickFix data demonstrates the ability to classify issues by reviewing 99.5% less features than state-of-the-art without sacrificing accuracy. 

Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processing methods are necessary to maintain citizens' satisfaction with their local governments. Herein, an optimal stopping framework is proposed to process urban issue requests quickly and accurately. The optimal classification and stopping rules are derived, and significant reduction in time-to-decision without sacrificing accuracy is demonstrated on a real-world dataset from SeeClickFix.