We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets (“bags”) of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.
|Title of host publication
|Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
|Niels Landwehr, Jilles Giuseppe, Giuseppe Manco, Paolo Frasconi
|Number of pages
|Published - 2016
|15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy
Duration: 19 Sep 2016 → 23 Sep 2016
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
|Riva del Garda
|19/09/16 → 23/09/16
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2016.