On–The–Fly Feature Selection and Classification with Application to Civic Engagement Platforms
Yasitha Warahena Liyanage, Daphney–Stavroula Zois, Charalampos Chelmis · 2020
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.