A Comparison of Two Contributive Analysis Methods Applied to an ANN Modeling Facial Attractiveness

Abstract

Artificial neural networks (ANNs) are powerful predictors. ANNs, however, essentially function like 'black boxes' because they lack explanatory power regarding input contribution to the model. Various contributive analysis algorithms (CAAs) have been developed to apply to ANNs to illuminate the influences and interactions between the inputs and thus, to enhance understanding of the modeled function. In this study two CAAs were applied to an ANN modeling facial attractiveness. Conflicting results from these CAAs imply that more research is needed in the area of contributive analysis and that researchers should be cautious when selecting a CAA method