多标签基于实例学习的原型选择

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Panagiotis Filippakis, Stefanos Ougiaroglou, Georgios Evangelidis
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引用次数: 0

摘要

为了提高基于实例的分类器的效率,同时保持较高的分类精度,一种被广泛采用的做法是减少训练集的大小,即用压缩集替换训练集。这个目标可以通过使用数据简化技术来实现,也称为原型选择或生成算法。虽然文献中有许多算法可以有效地解决单标签分类问题,但大多数算法不适用于多标签数据,因为一个实例可以属于多个类。由于不同的原因,众所周知的转换方法不能与数据约简技术相结合。压缩最近邻规则是一种流行的无参数单标签原型选择算法。IB2算法是精简最近邻规则的单遍变体。本文针对多标签数据提出了这些算法的变体。通过对9个不同的数据集进行的实验研究以及统计测试,我们证明了8种提出的方法(每种算法4种)在不影响分类准确性的情况下提供了显着的减少率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prototype Selection for Multilabel Instance-Based Learning
Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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