{"title":"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","authors":"T. Woolman, J.L. Pickard","doi":"10.4108/eetcasa.v8i24.1996","DOIUrl":null,"url":null,"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.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"49 1","pages":"e7"},"PeriodicalIF":1.1000,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition\",\"authors\":\"T. Woolman, J.L. Pickard\",\"doi\":\"10.4108/eetcasa.v8i24.1996\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":43034,\"journal\":{\"name\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"volume\":\"49 1\",\"pages\":\"e7\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eetcasa.v8i24.1996\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Scalable Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetcasa.v8i24.1996","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Gradient Descent Machine Learning with Equivalency Testing for Non-Subject Dependent Applications in Human Activity Recognition
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.