多类概率分类器的功率控制可靠性评估

IF 1.4 4区 计算机科学 Q2 STATISTICS & PROBABILITY
Hyukjun Gweon
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引用次数: 1

摘要

在多类别分类中,概率分类器的输出是类别的概率分布。在这项工作中,我们专注于对多类问题的概率分类器的可靠性进行统计评估。我们的方法基于预测空间中的k近邻生成Pearson(\chi^2)统计量。此外,我们开发了一种贝叶斯方法来估计可靠性测试的预期功率,该方法可用于适当的样本量k。我们提出了一种采样算法,并证明该算法获得了有效的先验分布。通过仿真研究评估了所提出的可靠性测试的有效性和预期功率。我们还提供了所提出的方法的示例和实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A power-controlled reliability assessment for multi-class probabilistic classifiers

A power-controlled reliability assessment for multi-class probabilistic classifiers

In multi-class classification, the output of a probabilistic classifier is a probability distribution of the classes. In this work, we focus on a statistical assessment of the reliability of probabilistic classifiers for multi-class problems. Our approach generates a Pearson \(\chi ^2\) statistic based on the k-nearest-neighbors in the prediction space. Further, we develop a Bayesian approach for estimating the expected power of the reliability test that can be used for an appropriate sample size k. We propose a sampling algorithm and demonstrate that this algorithm obtains a valid prior distribution. The effectiveness of the proposed reliability test and expected power is evaluated through a simulation study. We also provide illustrative examples of the proposed methods with practical applications.

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来源期刊
CiteScore
3.40
自引率
6.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.
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