Machine Learning ( IF 2.809 ) Pub Date : 2019-10-30 , DOI: 10.1007/s10994-019-05854-7
Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki

Abstract We study a bad arm existence checking problem in a stochastic K-armed bandit setting, in which a player’s task is to judge whether a positive arm exists or all the arms are negative among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold $$\theta _U$$, and it is negative if that is less than another given threshold $$\theta _L(\le \theta _U)$$. This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative arms’ roles; finding one positive arm is enough to judge positive existence while all the arms must be discriminated as negative to judge whole negativity. In the case with $$\varDelta =\theta _U-\theta _L>0$$, we propose elimination algorithms with arm selection policy (policy to determine the next arm to draw) and decision condition (condition to conclude positive arm’s existence or the drawn arm’s negativity) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.

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