An Average-Case Analysis of the k-Nearest Neighbar Classifier for Noisy Domains
An Average-Case Analysis of the k-Nearest Neighbar Classifier for Noisy Domains
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发表时间：
1997-08
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通讯作者：
Seishi Okamoto;N. Yugami
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作者：
Seishi Okamoto;N. Yugami
本文提出了一种平均情况下分析的fc-最近邻分类器（k-NN）。我们的分析涉及m-of-n//概念，并处理三种类型的噪声：相关属性噪声，不相关属性噪声和类噪声。我们正式计算的预期分类精度的fc-NN后，一定的固定数量的训练实例。该精度表示为域特性的函数。然后，通过使用准确度函数来探索fc-NN对每种类型的噪声的预测行为。我们研究的分类精度的fc-NN在不同的噪声水平，并显示如何噪声影响的fc-NN的准确性。我们还展示了噪声域中k的最佳值与训练实例数之间的关系。我们的分析是支持Monte Carlo模拟。
This paper presents an average-case analysis of the fc-nearest neighbor classifier (k-NN). Our analysis deals with m-of-n// concepts, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally compute the expected classification accuracy of fc-NN after a certain fixed number of training instances. This accuracy is represented as a function of the domain characteristics. Then, the predicted behavior of fc-NN for each type of noise is explored by using the accuracy function. We examine the classification accuracy of fc-NN at various noise levels, and show how noise affects the accuracy of fc-NN. We also show the relationship between the optimal value of k and the number of training instances in noisy domains. Our analysis is supported with Monte Carlo simulations.