Instance Selection: A Bayesian Decision Theory Perspective

Optimal instance selection for improved decision tree

Instance selection plays an important role in improving scalability of data mining algorithms, but it can also be used to improve the quality of the data mining results. In this dissertation we present a new optimization-based approach for instance selection that uses a genetic algorithm (GA) to select a subset of instances to produce a simpler decision tree with acceptable accuracy. The result...

A Unied Bayesian Decision Theory

This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences de…ned on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Je¤rey and others can be stated and compared, but also allows for the po...

Instance selection for simplified decision trees through the generation and selection of instance candidate subsets

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Bayesian Decision Making Theory

A Minimal Extension of Bayesian Decision Theory

Savage denied that Bayesian decision theory applies in large worlds. This paper proposes a minimal extension of Bayesian decision theory to a large-world context that evaluates an event E by assigning it a number π(E) that reduces to an orthodox probability for a class of measurable events. The Hurwicz criterion evaluates π(E) as a weighted arithmetic mean of its upper and lower probabilities, ...