Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-07-15 DOI:10.4108/eetcasa.v8i24.1996
T. Woolman, J.L. Pickard
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引用次数: 0

Abstract

INTRODUCTION: A solution to subject-independent HAR prediction through machine learning classification algorithms using statistical equivalency for comparative analysis between independent groups with non-subject training dependencies.OBJECTIVES: To indicate that the multinomial predictive classification model that was trained and optimized on the one-subject control group is at least partially extensible to multiple independent experiment groups for at least one activity class.METHODS: Gradient boosted machine multinomial classification algorithm is trained on a single individual with the classifier trained on all activity classes as a multinomial classification problem.RESULTS: Levene-Wellek-Welch (LWW) Statistic calculated as 0.021, with a Critical Value for LWW of 0.026, using an alpha of 0.05.CONCLUSION: Confirmed falsifiability that incorporates reproducible methods into the quasi-experiment design applied to the field of machine learning for human activity recognition.
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基于等效性测试的梯度下降机器学习在人类活动识别中的应用
简介:通过机器学习分类算法,利用统计等效性对具有非主题训练依赖性的独立组进行比较分析,解决独立于主题的HAR预测。目的:表明在单受试者对照组上训练和优化的多项预测分类模型至少部分可扩展到至少一个活动类的多个独立实验组。方法:梯度增强机器多项式分类算法在单个个体上训练,分类器在所有活动类上训练作为一个多项式分类问题。结果:Levene-Wellek-Welch (LWW)统计量为0.021,临界值为0.026,alpha为0.05。结论:证实可证伪性,将可重复方法纳入准实验设计,应用于人类活动识别的机器学习领域。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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