Weighted decision trees where the cost of a test depends on its outcome
Matthew J. Bennett · Applied Stochastic Models and Data Analysis · 1987
Abstract The problem of choosing strategies which minimize the cost of decision tree testing is considered in the case where the cost of each test depends on its outcome. The classical entropy bound on the cost is derived and we propose an algorithm for decision tree design based on an extension of Huffman's construction. By comparing the mean costs for a large sample of randomly chosen situations, we show that this algorithm performs well compared with a number of previously published algorithms. The algorithms are also compared with optimal performance for a range of simple cases.