.png){kind=icon}
返回
Evolutionary dataset optimisation: learning algorithm quality through evolution
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-019-01592-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the 'best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric. These datasets can be studied so as to learn what attributes lead to a particular progression of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a case study in clustering is presented. This case study demonstrates the performance and nuances of the method which we call Evolutionary Dataset Optimisation. In this study, a number of known properties about preferable datasets for the clustering algorithms known as k-means and DBSCAN are realised in the generated datasets.
Keyword:
Evolutionary algorithm
Optimisation
Algorithm design
Artificial data generation
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:
3.5
论文数:
7.6K
被引数:
1.7W
机构
暂无机构信息
引用论文
Performance Assessment of Different Roof Integrated Photovoltaic Modules under Mediterranean ClimateOAAI
.png){kind=icon}
OAAI分享收藏分享收藏分享收藏Determination of apovincaminic acid in serum by means of high-performance liquid chromatography
PREAI
分享收藏分享收藏Potential Negative Consequences of Adding Phosphorus‐Based Fertilizers to Immobilize Lead in Soil
PREAI
分享收藏
查看更多内容