Reduction techniques for instance-based learning algorithms
Reduction techniques for instance-based learning algorithms
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DOI：
10.1023/a:1007626913721
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发表时间：
2000-03-01
期刊：
影响因子：
7.5
通讯作者：
Martinez, TR
中科院分区：
文献类型：
--
作者：
Wilson, DR;Martinez, TR
基于实例的学习算法经常面临的问题，决定哪些实例存储在泛化过程中使用。存储过多的实例可能会导致内存需求过大和执行速度缓慢，并可能导致对噪声过度敏感。本文有两个主要目的。首先，它提供了一个调查现有的算法，用于减少存储需求的基于实例的学习算法和其他基于范例的算法。其次，它提出了六个额外的约简算法，称为DROP 1-DROP 5和DEL（其中三个在Wilson & Martinez，1997 c中首次描述为RT 1-RT 3），可以用来从概念描述中删除实例。这些算法和10个算法的调查进行了比较31分类任务。在这些提供大量存储减少的算法中，DROP算法在这些实验中具有最高的平均泛化精度，特别是在存在均匀类噪声的情况下。
Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many instances can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms and other exemplar-based algorithms. Second, it proposes six additional reduction algorithms called DROP1-DROP5 and DEL (three of which were first described in Wilson & Martinez, 1997c, as RT1-RT3) that can be used to remove instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 classification tasks. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.