A lazy approach to pruning classification rules

Elena Baralis, P. Garza
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引用次数: 121

Abstract

Associative classification is a promising technique for the generation of highly precise classifiers. Previous works propose several clever techniques to prune the huge set of generated rules, with the twofold aim of selecting a small set of high quality rules, and reducing the chance of overfitting. In this paper, we argue that pruning should be reduced to a minimum and that the availability of a large rule base may improve the precision of the classifier without affecting its performance. In L/sup 3/ (Live and Let Live), a new algorithm for associative classification, a lazy pruning technique iteratively discards all rules that only yield wrong case classifications. Classification is performed in two steps. Initially, rules which have already correctly classified at least one training case, sorted by confidence, are considered If the case is still unclassified, the remaining rules (unused during the training phase) are considered, again sorted by confidence. Extensive experiments on 26 databases from the UCI machine learning database repository show that L/sup 3/ improves the classification precision with respect to previous approaches.
一种惰性方法来修剪分类规则
关联分类是一种很有前途的生成高精度分类器的技术。先前的工作提出了几种巧妙的技术来修剪大量生成的规则集,其双重目的是选择一组小的高质量规则集,并减少过度拟合的机会。在本文中,我们认为修剪应该减少到最低限度,并且大规则库的可用性可以在不影响其性能的情况下提高分类器的精度。在一种新的关联分类算法L/sup 3/ (Live and Let Live)中,惰性剪枝技术迭代地丢弃了所有只产生错误分类的规则。分类分两个步骤执行。首先,考虑已经正确分类了至少一个训练案例的规则,并按置信度排序。如果该案例仍未分类,则考虑剩余的规则(在训练阶段未使用),再次按置信度排序。在UCI机器学习数据库库的26个数据库上进行的大量实验表明,与以前的方法相比,L/sup 3/提高了分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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