Gamification for machine learning: The classification game

TY - GEN

T1 - Gamification for machine learning

T2 - 3rd International Workshop on Gamification for Information Retrieval, GamifIR 2016

AU - Nunzio, Giorgio Maria Di

AU - Maistro, Maria

AU - Zilio, Daniel

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The creation of a labelled dataset for machine learning purposes is a costly process. In recent works, it has been shown that a mix of crowdsourcing and active learning approaches can be used to annotate objects at an affordable cost. In this paper, we study the gamification of machine learning techniques; in particular, the problem of classification of objects. In this first pilot study, we designed a simple game, based on a visual interpretation of probabilistic classifiers, that consists in separating two sets of coloured points on a two-dimensional plane by means of a straight line. We present the current results of this first experiment that we used to collect the requirements for the next version of the game and to analyze i) what is the 'price' to build a reasonably accurate classifier with a small amount of labelled objects, ii) and compare the accuracy of the player to the state-of-the-art classification algorithms.

AB - The creation of a labelled dataset for machine learning purposes is a costly process. In recent works, it has been shown that a mix of crowdsourcing and active learning approaches can be used to annotate objects at an affordable cost. In this paper, we study the gamification of machine learning techniques; in particular, the problem of classification of objects. In this first pilot study, we designed a simple game, based on a visual interpretation of probabilistic classifiers, that consists in separating two sets of coloured points on a two-dimensional plane by means of a straight line. We present the current results of this first experiment that we used to collect the requirements for the next version of the game and to analyze i) what is the 'price' to build a reasonably accurate classifier with a small amount of labelled objects, ii) and compare the accuracy of the player to the state-of-the-art classification algorithms.

UR - http://www.scopus.com/inward/record.url?scp=84985919823&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84985919823

SN - 1613-0073

VL - 1642

SP - 45

EP - 52

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 21 July 2016

ER -