A learning fuzzy decision tree and its application to tactile image

Decision trees play important roles in many fields such as pattern recognition and classification It is because they have simple, apparent and fast reasoning process. This paper develops an algorithm to generate a learning fuzzy decision tree. This algorithm firstly collects enough training data for generating a practical decision tree. It then uses fuzzy statistics to calculate fuzzy sets for representing the training data in order to save computing memory and increase generation speed. Finally, this algorithm uses a suboptimal criterion to learn a decision tree from the resultant fuzzy sets. The algorithm is applied to a general-purpose tactile force sensing system. This system uses fuzzy logic to interpolate the force data. Then, the proposed algorithm is used to generate the desired decision tree from the tactile data. Based on the decision tree, the objects can be online recognized precisely.