Use of Sequential Bayes with Class Probability Trees
Donald Michie, Ayyaj Attar · 1991
Abstract For building classifiers from data, rule induction has established itself as an alternative to multivariate statistical approaches, including those of ‘neural’ computing. But modern rule-induction algorithms such as CART and C4 have not yet found a fully satisfactory way of discriminating logical from statistical forms of complexity in data. Systems of sequential Bayes rules offer a less ad hoc basis for combining probabilistic with rule-based approaches. In Evidencer, tree-structured rules are linked in a PROSPECTOR-like procedural hierarchy, and updated and processed according to a thresholding regime adapted from Wald’s sequential analysis (1947). The resulting formalism steers a course between the oversimplifications of PROSPECTOR and the complexity of full multi-level Bayes, while retaining precise error bounds on decisions.