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首页> 外文会议>International symposium on intelligent data analysis >Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML
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Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML

机译：不要过早排除简单模型：OpenML中比较线性和非线性分类器的大规模基准

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摘要

A basic step for each data-mining or machine learning task is to determine which model to choose based on the problem and the data at hand. In this paper we investigate when non-linear classifiers outperform linear classifiers by means of a large scale experiment. We benchmark linear and non-linear versions of three types of classifiers (support vector machines; neural networks; and decision trees), and analyze the results to determine on what type of datasets the non-linear version performs better. To the best of our knowledge, this work is the first principled and large scale attempt to support the common assumption that non-linear classifiers excel only when large amounts of data are available.
机译：每个数据挖掘或机器学习任务的基本步骤是根据问题和手头的数据确定选择哪种模型。在本文中，我们将通过大规模实验研究何时非线性分类器优于线性分类器。我们对三种类型的分类器（支持向量机，神经网络和决策树）的线性和非线性版本进行基准测试，并分析结果以确定非线性版本在哪种类型的数据集上表现更好。据我们所知，这项工作是首次原理性的大规模尝试，它支持非线性分类器仅在有大量数据可用时才占优势的普遍假设。

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