Reduction Techniques for Instance-based Learning Algorithms

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Reduction Techniques for Instance-based Learning Algorithms (2000)

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by D. Randall Wilson , Tony R. Martinez

Venue:	MACHINE LEARNING
Citations:	213 - 3 self

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@INPROCEEDINGS{Wilson00reductiontechniques,
    author = {D. Randall Wilson and Tony R. Martinez},
    title = {Reduction Techniques for Instance-based Learning Algorithms},
    booktitle = {MACHINE LEARNING},
    year = {2000},
    pages = {257--286},
    publisher = {}
}

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Abstract

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.

Keyphrases

instance-based learning algorithm reduction technique substantial storage reduction drop algorithm main purpose drop1 drop5 concept description many instance large memory requirement exemplar-based algorithm average generalization accuracy slow execution speed additional reduction algorithm storage requirement wilson martinez classification task rt1 rt3 uniform class noise

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