Proteus: A scalable, flexible and extensible multi-classifier framework

D. Winiarski, Y. Coady
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Abstract

Though the popularity and demand for machine learning infrastructures is soaring in this age of “big data”, general purpose configuration and deployment strategies are still in their infancy. This paper presents Proteus, a flexible and extensible framework allowing different machine learning algorithms to be introduced in a plug-and-play manner in order to be evaluated. Proteus enables domain experts to more easily compare, contrast, and even combine results from classifiers including Deep Learning, GLM, GBM, Naive Bayes, Random Forest, SVM and Linear Regression. Leveraging this design, it is easier to explore the possibility that a combination of multiple classifiers may be the best approach to guaranteeing high accuracy. A case study involving 6 months of mouse-movement data from 5 patients with a Clinical Dementia Rating (CDR) of 0 (control group) and 5 patients with a CDR of 0.5 (considered a high impairment level) identifies the costs and benefits of this engineering effort towards a scalable, flexible and extensible architecture for multi-classifier analysis.
Proteus:一个可伸缩的、灵活的、可扩展的多分类器框架
尽管在这个“大数据”时代,机器学习基础设施的普及和需求正在飙升,但通用配置和部署策略仍处于起步阶段。本文介绍了Proteus,这是一个灵活且可扩展的框架,允许以即插即用的方式引入不同的机器学习算法,以便进行评估。Proteus使领域专家能够更轻松地比较,对比甚至组合来自分类器的结果,包括深度学习,GLM, GBM,朴素贝叶斯,随机森林,支持向量机和线性回归。利用这种设计,可以更容易地探索多个分类器的组合可能是保证高准确性的最佳方法的可能性。一项涉及5名临床痴呆评分(CDR)为0(对照组)和5名CDR为0.5(被认为是高损伤水平)的患者6个月的鼠标运动数据的案例研究,确定了这种工程努力的成本和收益,以实现可扩展、灵活和可扩展的多分类器分析架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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