Crowdsourced Data Analytics: A Case Study of a Predictive Modeling Competition

Authors

  • Yukino Baba National Institute of Informatics
  • Nozomi Nori Kyoto University
  • Shigeru Saito OPT, Inc.
  • Hisashi Kashima Kyoto University

Abstract

Predictive modeling competitions provide a new data mining approach that leverages crowds of data scientists to examine a wide variety of predictive models and build the best performance model. In this paper, we report the results of a study conducted on CrowdSolving, a platform for predictive modeling competitions in Japan. We hosted a competition on a link prediction task and observed that (i) the prediction performance of the winner significantly outperformed that of a state-of-the-art method, (ii) the aggregated model constructed from all submitted models further improved the final performance, and (iii) the performance of the aggregated model built only from early submissions nevertheless overtook the final performance of the winner.

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Published

2014-09-05

How to Cite

Baba, Y., Nori, N., Saito, S., & Kashima, H. (2014). Crowdsourced Data Analytics: A Case Study of a Predictive Modeling Competition. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1). Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/13194