Tabu search-based classification for eye-movement behavioral decisions

Abstract

Adaptive human–computer interfaces (HCIs) are fundamental to designing adaptive websites and adaptive decision support systems. Integrating these intelligent systems with modern eye trackers provides more effective ways to exploit eye fixation data and offers improved services to users. We develop an exemplar-based classifier using the tabu search algorithm to predict which decision strategy may underlie an empirical search behavior. Our algorithm reduces the size of decision concept representations to find the best exemplars for each concept. Experimental results show that our classifier is highly accurate in classifying the sequence of empirical eye fixations, demonstrating the promise of integrating adaptive HCIs with modern eye trackers.