Overfitting revisited: an information-theoretic approach to simplifying discrimination trees
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A method of simplifying inductively generated discrimination trees using a measure of tree quality based on the principle of information economy, which takes into account both the size of the tree and thesize of the outcome data after (notional) encoding by that tree.
Abstract
Abstract This paper describes a method of simplifying inductively generated discrimination trees using a measure of tree quality based on the principle of information economy, which takes into account both the size of the tree and the size of the outcome data after (notional) encoding by that tree. Results of testing this method on a selection of data sets show that it has some practical advantages over previously used techniques for tree-pruning. Some of the theoretical implications of the present method are also discussed. Keywords: machine learningdata compressioninductive inferenceinformation theorydiscrimination treespattern classification Notes tel: +44 (0)272-656261 (extn. 3134) tel: +44 (0)602-515284 tel: +44 (0)786-467659