DocumentCode :
3576372
Title :
Crowdsourced data analytics: A case study of a predictive modeling competition
Author :
Baba, Yukino ; Nori, Nozomi ; Saito, Shigeru ; Kashima, Hisashi
Author_Institution :
Nat. Inst. of Inf., JST, Kawarabayashi, Japan
fYear :
2014
Firstpage :
284
Lastpage :
289
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. Competition hosts, who provide their own dataset and specify the problem to be solved, are not only able to obtain the best model from among those submitted but also to aggregate the submitted models to obtain one that outperforms the rest. 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. Our results show the power of crowds for predictive modeling, not only in the quality of the obtained model, but also in its speed to achieve it. Furthermore, they demonstrate the possibilities of combining human insights and machine learning in data analytics.
Keywords :
data analysis; data mining; learning (artificial intelligence); CrowdSolving; Japan; crowdsourced data analytics; data mining; link prediction task; machine learning; predictive modeling competition; predictive modeling competitions; Analytical models; Atmospheric measurements; Data models; Logic gates; Particle measurements; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Science and Advanced Analytics (DSAA), 2014 International Conference on
Type :
conf
DOI :
10.1109/DSAA.2014.7058086
Filename :
7058086
Link To Document :
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