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首页> 外文会议>PRICAI'98 : Topics in artificial intelligence >Multi-layer Incremental Induction
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Multi-layer Incremental Induction

机译：多层增量感应

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摘要

This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an existing nonincremental induction algorithm to learn incrementally from noisy data. MLII makes use of three operations: data partitioning, generalization and reduction. Generalization can either learn a set of rules from a (sub)set of examples, or refine a previous set of rules. The latter is achieved through a re-description operation called reduction: from a set of examples and a set of rules, we derive a new set of examples describing the behaviour of the rule set. New rules are extracted from these behavioral examples, and these rules can be seen as meta-rules, as they control previous rules in order to improve their predictive accuracy. Experimental results show that MLII achieves significant improvement on the existing nonincremental algorithm HCV used for experiments in this paper, in terms of rule accuracy.
机译：本文介绍了一种多层增量式归纳算法MLII，该算法与现有的非增量式归纳算法链接以从噪声数据中逐步学习。 MLII使用三个操作：数据分区，泛化和归约。泛化既可以从（子）示例集中学习一组规则，也可以完善先前的一组规则。后者是通过称为减少的重新描述操作实现的：从一组示例和一组规则中，我们得出了一组描述规则集行为的新示例。从这些行为示例中提取了新规则，这些规则可以视为元规则，因为它们控制以前的规则以提高其预测准确性。实验结果表明，就规则精度而言，MLII相对于本文中用于实验的现有非增量算法HCV进行了重大改进。

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  • 来源
  • 会议地点 Singapore(SG);Singapore(SG)
  • 作者

    Xindong Wu; William H.W. Lo;

  • 作者单位

    School of Computer Science and Software Ebgineering Monash University 900 Dandenong Road Melbourne, VIC 3145, Australia;

    School of Computer Science and Software Ebgineering Monash University 900 Dandenong Road Melbourne, VIC 3145, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化系统理论;
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