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Top-Down Induction of Fuzzy Pattern Trees

机译：模糊模式树的自顶向下归纳

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摘要

Fuzzy pattern tree induction was recently introduced as a novel machine learning method for classification. Roughly speaking, a pattern tree is a hierarchical, tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. A pattern-tree classifier is composed of an ensemble of such pattern trees: one for each class label. This type of classifier is interesting for several reasons. For example, since a single pattern tree can be considered as a kind of logical description of a class, it is quite appealing from an interpretation point of view. Moreover, in terms of classification accuracy, the method has shown promising performance in first experimental studies. Yet, as will be argued in this paper, the algorithm that has originally been proposed for learning fuzzy pattern trees from data offers scope for improvement. Here, we propose a new method that modifies the original proposal in several ways. Notably, our learning algorithm reverses the direction of pattern tree construction from bottom-up to top-down. Additionally, a different termination criterion is proposed that is more adapted to the learning problem at hand. Experimentally, it will be shown that our approach is indeed able to outperform the original learning method in terms of predictive accuracy.
机译：最近引入了模糊模式树归纳法作为一种新颖的分类机器学习方法。粗略地说，模式树是一种分层的树状结构，其内部节点用广义（模糊）逻辑运算符标记，并且其叶子节点与输入属性上的模糊谓词关联。模式树分类器由这种模式树的集合组成：每个类标签一个。这种分类器很有趣，原因有几个。例如，由于可以将单个模式树视为类的一种逻辑描述，因此从解释的角度来看，它非常吸引人。此外，就分类准确性而言，该方法在首次实验研究中已显示出令人鼓舞的性能。但是，正如本文将要讨论的那样，最初提出的用于从数据中学习模糊模式树的算法提供了改进的空间。在这里，我们提出了一种新方法，可以通过多种方式修改原始提案。值得注意的是，我们的学习算法将模式树构造的方向从下至上转换为自上而下。另外，提出了不同的终止准则，该准则更适合当前的学习问题。从实验上可以看出，我们的方法在预测准确性方面确实能够胜过原始学习方法。

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