Medical and Behavioral Knowledge Discovery using Multi-Objective Analysis

Objective ranking and clustering of behavioral data that utilized different metrics is an important foundation for understanding the relationship between behavior and physiological as well as neurological processes in medicine and neuroscience. Fortunately, similar problems have been extensively studied in multi-objective optimization, where a population of solutions needs to be ranked in terms of performance within a high dimensional objective space. In this paper, we take advantage of the ordinal nature of behavioral metrics, and propose a non-dominated ranking-based approach to perform Pareto dominance-based partial ranking on the test subjects of behavioral studies regarding their performances with respect to multiple metrics. We have also proposed two indicators, interfront hypervolume and intrafront spread, to measure the separation between and diversity within non-dominated fronts. They serve to gauge the validity of the produced fronts as a basis for clustering analysis. Our approach is then applied to two different datasets and its functionalities and potentials are demonstrated.