Abstract

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.

中文翻译：

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进化数据集优化：通过进化学习算法质量

摘要

在本文中，我们提出了一种学习算法如何执行的新颖方法。传统上，基于一些固定指标，在有限数量的现有（或新模拟的）基准数据集上对算法进行比较。具有该度量的最小值的算法被选择为“性能最佳”。我们提供了一种新的方法来颠覆这种范式。相反，我们旨在通过通过遗传进化生成人工数据来获得算法性能的更丰富的图片，其目的是创建特定算法在给定指标上表现良好的数据集。可以研究这些数据集，以了解哪些属性导致给定算法的特定进展。在详细描述算法并简要描述了开源实现之后，提出了一个案例研究。此案例研究证明了我们称为“进化数据集优化”的方法的性能和细微差别。在这项研究中，有关聚类算法的首选数据集的许多已知属性称为在生成的数据集中实现k-均值和DBSCAN。