Showing papers on "Decision tree learning published in 1973"

A Partitioning Algorithm with Application in Pattern Classification and the Optimization of Decision Trees

Abstract: The efficient partitioning of a finite-dimensional space by a decision tree, each node of which corresponds to a comparison involving a single variable, is a problem occurring in pattern classification, piecewise-constant approximation, and in the efficient programming of decision trees A two-stage algorithm is proposed The first stage obtains a sufficient partition suboptimally, either by methods suggested in the paper or developed elsewhere; the second stage optimizes the results of the first stage through a dynamic programming approach In pattern classification, the resulting decision rule yields the minimum average number of calculations to reach a decision In approximation, arbitrary accuracy for a finite number of unique samples is possible In programming decision trees, the expected number of computations to reach a decision is minimized

Representation and Analysis of Multi-Stage Problems in R & D

Abstract: The paper describes a method of representing and analysing the multi-stage, dynamic nature of many types of research and development projects. The method makes use of a number of well-known techniques, decision trees, mathematical programming and simulation in combination. Each project which could form part of a laboratory portfolio is represented in a modified decision tree format called a “project tree,” and the project trees are then considered in one analysis based on heuristics. The approach is demonstrated using a small illustrative example, and an industrial case study is described, giving attention to computational problems of size.