Efficient decision tree design for discrete variable pattern recognition problems

An algorithm is developed for the design of an efficient decision tree with application to the pattern recognition problems involving discrete variables. The problem of evaluating an extremely large number of trees in search of a minimum cost decision tree is tackled by defining a criterion to estimate the minimum expected cost of a tree in terms of the weights of its terminal nodes and costs of the measurements, which then is used to establish the search procedure for the efficient decision tree. The concept of prime events is used to obtain the number of modes and the corresponding weights in the design samples. An application of the proposed algorithm is presented for the design of an efficient decision tree for classifying Devanagri numerals.