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In this paper, we present two approaches and algorithms that adapt areas ofinterest (AOI) or regions of interest (ROI), respectively, to the eye trackingdata quality and classification task. The first approach uses featureimportance in a greedy way and grows or shrinks AOIs in all directions. Thesecond approach is an extension of the first approach, which divides the AOIsinto areas and calculates a direction of growth, i.e. a gradient. Bothapproaches improve the classification results considerably in the case ofgeneralized AOIs, but can also be used for qualitative analysis. In qualitativeanalysis, the algorithms presented allow the AOIs to be adapted to the data,which means that errors and inaccuracies in eye tracking data can be bettercompensated for. A good application example is abstract art, where manual AOIsannotation is hardly possible, and data-driven approaches are mainly used forinitial AOIs.  Link:https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FAOIGradient&mode=list

Area of interest adaption using feature importance