An experimental evaluation of comprehensibility aspects of knowledge structures derived through induction techniques: a case study of industrial fault diagnosis

Machine induction has been extensively used in order to develop knowledge bases for decision support systems and predictive systems. The extent to which developers and domain experts can comprehend these knowledge structures and gain useful insights into the basis of decision making has become a challenging research issue. This article examines the knowledge structures generated by the C4.5 induction technique in a fault diagnostic task and proposes to use a model of human learning in order to guide the process of making comprehensive the results of machine induction. The model of learning is used to generate hierarchical representations of diagnostic knowledge by adjusting the level of abstraction and varying the goal structures between 'shallow' and 'deep' ones. Comprehensibility is assessed in a global way in an experimental comparison where subjects are required to acquire the knowledge structures and transfer to new tasks. This method of addressing the issue of comprehensibility appears promising especially for machine induction techniques that are rather inflexible with regard to the number and sorts of interventions allowed to system developers.