Cellular Tree Classifiers

Gérard Biau; Luc Devroye

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arXiv · 1301.4679

Cellular Tree Classifiers

Gérard Biau·Luc Devroye

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Abstract

The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classification, or shall one split the data into two or more parts and send each part to a potentially different computer (or cell) for further processing? At first sight, it seems impossible to define with this paradigm a consistent classifier as no cell knows the "original data size", $n$. However, we show that this is not so by exhibiting two different consistent classifiers. The consistency is universal but is only shown for distributions with nonatomic marginals.

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BibTeX RIS

Gérard Biau, Luc Devroye. 2013-06-25. Cellular Tree Classifiers. https://arxiv.org/abs/1301.4679

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