基于多目标进化算法的分类核心集发现
Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
摘要：
核心集是训练集的一个子集，使用它，机器学习算法获得的性能与在整个原始数据上训练时的性能相似。Coreset的发现是一个积极和开放的研究方向，因为它可以提高算法的训练速度，并可能帮助人类理解结果。在前人工作的基础上，提出了一种新的方法：对候选紧身胸衣进行迭代优化，添加和删除样本。由于限制训练规模和结果质量之间存在明显的权衡，使用多目标进化算法同时最小化集合中的点的数量和分类误差。非平凡基准测试的实验结果表明，所提出的方法是能够提供的结果，允许分类器，以获得更低的错误和更好的能力概括看不见的数据比国家的最先进的核心发现技术。
英文摘要：
A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data. Coreset discovery is an active and open line of research as it allows improving training speed for the algorithms and may help human understanding the results. Building on previous works, a novel approach is presented: candidate corsets are iteratively optimized, adding and removing samples. As there is an obvious trade-off between limiting training size and quality of the results, a multi-objective evolutionary algorithm is used to minimize simultaneously the number of points in the set and the classification error. Experimental results on non-trivial benchmarks show that the proposed approach is able to deliver results that allow a classifier to obtain lower error and better ability of generalizing on unseen data than state-of-the-art coreset discovery techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
