Psychological measure on fish catches and its application to optimization criterion for machine-learning-based predictors

Abstract

Psychological fish catches are designed and successfully applied to an optimization criterion for machine-learning-based predictors. In automatic fish catch prediction, prediction errors allowed by fishery workers differ depending on fish catches. Such error tolerance has not been considered both in evaluating prediction results and in training predictors. In the present study, the investigation of fishermen’s tolerance in prediction errors using a psychophysics method and subsequently reflecting it in constructing a psychological measure on fish catches is performed. In addition, the psychological measure obtained is exploited in the optimization of fish catch prediction models to provide forecasts intuitive to fishery workers.