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Assessing Luhmann’s Social Theory. Show all authors. John Mingers.
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An Empirical Comparison of Selection Measures for Decision-Tree Induction
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One approach to induction is to develop a decision tree from a set of examples. When used with noisy rather than deterministic data, the method involves three main stages — creating a complete tree able to classify all the examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. Some problems encountered at this stage are missing data and multi-valued attributes. The paper considers a number of different measures and experimentally examines their behavior in four domains.
The results show that the choice of measure affects the size of a tree but not its accuracy, which remains the same even when attributes are selected randomly. Download to read the full article text. Bratko, I. Learning diagnostic rules from incomplete and noisy data. Seminar on AI Methods in Statistics. London: Unicom Seminars Ltd.