论文标题
概念上的最小分类以及多维适应和性能保证
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
论文作者
论文摘要
实例标签对的统计特征在监督分类的实际情况下通常会随着时间而变化。传统的学习技术通过精心选择的学习率，遗忘因素或窗口大小来适应这种概念漂移的变化率。但是，常见场景的时间变化是多维的，即不同的统计特征通常会以不同的方式变化。本文介绍了自适应最小风险分类器（AMRC），这些风险分类器（AMRC）通过多元时间和高阶跟踪对时间变化的基础分布进行了多维时间的变化。此外，与常规技术不同，AMRC可以提供可计算的紧缩性能保证。在多个基准数据集上的实验显示，与最先进的AMRC的分类改进以及提出的性能保证的可靠性相比。
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate of change by means of a carefully chosen learning rate, forgetting factor, or window size. However, the time changes in common scenarios are multidimensional, i.e., different statistical characteristics often change in a different manner. This paper presents adaptive minimax risk classifiers (AMRCs) that account for multidimensional time changes by means of a multivariate and high-order tracking of the time-varying underlying distribution. In addition, differently from conventional techniques, AMRCs can provide computable tight performance guarantees. Experiments on multiple benchmark datasets show the classification improvement of AMRCs compared to the state-of-the-art and the reliability of the presented performance guarantees.
