The multitree pattern recognition algorithm proposed by Byung-Mu Kim[1] and Jeong-Hee Lee[2] is modified in order to improve its performance. The basic ideas of the multitree pattern classification algorithm are that the binary decision tree used to classify an unknown pattern is constructed for each feature and that at each stage, classification rule decides whether to classify the unknown pattern or to extract the feature value according to the feature order. So the feature ordering needed in the classification procedure is easy and the number of features used in the classification procedure are small compared with other classification algorithms. Thus the application of the algorithm can be utilized in a real pattern recognition problem even when the number of features and that of the classes are very large. In this paper, the weighting factor assignment scheme in the decision procedure is modified and various classification rules are proposed by means of the weighting factor. And the branch and bound method is applied to feature subset selection and feature ordering. Several experimental results show that the performance of the multitree pattern classification algorithm is improved by the proposed scheme.
