TY - GEN
T1 - Aggregating crowd opinions using shapley value regression
AU - Sakurai, Yuko
AU - Kawahara, Jun
AU - Oyama, Satoshi
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Crowdsourcing is becoming increasingly popular in various tasks. Aggregating answers from workers in crowdsouring has been a widely used technique for providing many applications and services. To aggregate these answers, fair evaluation of workers is important to motivate them to give high quality answers. However, it is difficult to fairly evaluate workers if their answers show a high degree of correlation. In this paper, we propose to use the Shapley value regression as a means to address this problem. The regression technique is based on ideas developed from cooperative game theory to evaluate the relative importance of explanatory variables in reducing the error. We also exploit sparseness of worker collaboration graph to effectively calculate the Shapley value, since it requires an exponential computation time to calculate the Shapley value.
AB - Crowdsourcing is becoming increasingly popular in various tasks. Aggregating answers from workers in crowdsouring has been a widely used technique for providing many applications and services. To aggregate these answers, fair evaluation of workers is important to motivate them to give high quality answers. However, it is difficult to fairly evaluate workers if their answers show a high degree of correlation. In this paper, we propose to use the Shapley value regression as a means to address this problem. The regression technique is based on ideas developed from cooperative game theory to evaluate the relative importance of explanatory variables in reducing the error. We also exploit sparseness of worker collaboration graph to effectively calculate the Shapley value, since it requires an exponential computation time to calculate the Shapley value.
UR - http://www.scopus.com/inward/record.url?scp=85057087491&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-03014-8_13
DO - 10.1007/978-3-030-03014-8_13
M3 - Conference contribution
AN - SCOPUS:85057087491
SN - 9783030030131
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 160
BT - Multi-disciplinary Trends in Artificial Intelligence - 12th International Conference, MIWAI 2018, Proceedings
A2 - Malaka, Rainer
A2 - Kaenampornpan, Manasawee
A2 - Nguyen, Duc Dung
A2 - Schwind, Nicolas
PB - Springer Verlag
T2 - 12th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2018
Y2 - 18 November 2018 through 20 November 2018
ER -