Case Based Learning for Structure of Decision Tree and Application to Human Assessment Knowledge

Abstract

This paper introduces a method for case based learning of human assessment knowledge, and it is applied to a prediction of driving dangers. The human knowledge is represented by using decision trees to classify the situation described by attributes which are based on conditional information into an appropriate class. The practical application of driving dangers demonstrates usefulness of the tree learning algorithm GAD (Genetic Algorithm based Decision Tree Learning) for the human assessment problem in case of implementation. GAD is expected to involve a high generalization ability that it is robust to the lack and bias of the data. The driving situation experiment is consisted of cases which are represented by the attributes which can be defined as the location and the move of obstructions and the driver's judgment of danger. We apply the proposed method with GAD to the driving assessment example after the definition of the attributes and the object ordering algorithm which defines the importance of the obstruction. The proposed method is considered in terms of the number of sample required for learning, the learning accuracy and the size of the result tree. We also demonstrate the comparison between the human assessment and the extracted knowledge by our method in certain situations.