Learning to Learn Decision Trees

Vlad G. Dabija, Katsuhiko Tsujino, Shogo Nishida

Decision trees are widely used in machine learning and knowledge acquisition systems. However, there is no optimal or even unanimously accepted strategy of obtaining "good" such trees, and most of the generated trees suffer from improprieties, i.e. inadequacies in representing knowledge. The final goal of the research reported here is to formulate a theory for the decision trees domain, that is a set of heuristics (on which a majority of experts will agree) which will describe a good decision tree, as well as a set of heuristics specifying how to obtain optimal trees. In order to achieve this goal we have designed a recursive architecture learning system, which monitors an interactive knowledge acquisition system based on decision trees and driven by explanatory reasoning, and incrementally acquires from the experts using it the knowledge used to build the decision trees domain theory. This theory is also represented as a set of decision trees, and may be domain dependent. Our system acquires knowledge to define the notion of good/bad decision trees and to measure their quality, as well as knowledge needed to guide domain experts in constructing good decision trees. The partial theory acquired at each moment is also used by the basic knowledge acquisition system in its tree generation process, thus constantly improving its performance.


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