Human-oriented information acquisition in sequential pattern classification: Part I — Single membership classification

Abstract

Information acquisition strategies which incorporate human heuristics are formulated for pattern classification tasks, and their effectiveness is evaluated. The heuristics are based on two observations: (1) human decision-makers tend to limit themselves to a subset of classes and to select features oriented toward this subject only; (2) human decision-makers typically use considerations related to the history of process, that is, to class probabilities in earlier stages, while classical Bayesian strategies consider only the current class probabilities. These heuristics are incorporated in four different strategies with which the authors experimented. The findings are useful for the development of decision aids whose information selection strategies may be tuned to the operator's information selection behaviour by offering the operator an aid which reflects his own information priorities.