.png){kind=icon}
返回
Improving nearest neighbor classification using Ensembles of Evolutionary Generated Prototype Subsets
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.03.015.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the most accurate types of prototype selection algorithms, preprocessing techniques that select a subset of instances from the data before applying nearest neighbor classification to it, are evolutionary approaches. These algorithms result in very high accuracy and reduction rates, but unfortunately come at a substantial computational cost. In this paper, we introduce a framework that allows to efficiently use the intermediary results of the prototype selection algorithms to further increase their accuracy performance. Instead of only using the fittest prototype subset generated by the evolutionary algorithm, we use multiple prototype subsets in an ensemble setting. Secondly, in order to classify a test instance, we only use prototype subsets that accurately classify training instances in the neighborhood of that test instance. In an experimental evaluation, we apply our new framework to four state-of-the-art prototype selection algorithms and show that, by using our framework, more accurate results are obtained after less evaluations of the prototype selection method. We also present a case study with a prototype generation algorithm, showing that our framework is easily extended to other preprocessing paradigms as well. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Classification
Evolutionary algorithms
K Nearest Neighbor
Ensembles
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
分享收藏Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them应用进化计算中计算实验的复制和比较: 常见陷阱和避免它们的准则
PREAI
分享收藏Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study使用进化算法作为KDD中数据约简的实例选择: 一项实验研究
PREAI
分享收藏
查看更多内容