Fuzzy output error as the performance function for training artificial neural networks to predict reading comprehension from eye gaze

Imbalanced data sets are common in real life and can have a negative effect on classifier performance. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed forward neural networks to overcome this problem. The imbalanced data sets we use are eye gaze data recorded from reading and answering a tutorial and quiz. The goal is to predict the quiz scores for each tutorial page. We show that the use of FOE as the performance function for training neural networks provides significantly better classification of eye movements to reading comprehension scores. A neural network with three hidden layers of neurons gave the best classification results especially when FOE was used as the performance function for training. In these cases, upwards of a 19% reduction in misclassification was achieved compared to using MSE as the performance function.