{"title":"人口统计属性预测中移动传感器数据的降噪","authors":"Itay Hazan, A. Shabtai","doi":"10.1109/MOBILESOFT.2015.25","DOIUrl":null,"url":null,"abstract":"In this paper we attempt demonstrate how we can use smartphone sensor data effectively for predicting gender. We specifically focus on sensor data that is assumed to inflict minimal risk to other applications, the system, or the user: Installed Applications, Network Traffic Amount, and Accelerometer readings. We propose several simple heuristics for pre-processing the data and for noise reduction which eventually results in improved accuracy in predicting gender.","PeriodicalId":131706,"journal":{"name":"2015 2nd ACM International Conference on Mobile Software Engineering and Systems","volume":"316 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Noise Reduction of Mobile Sensors Data in the Prediction of Demographic Attributes\",\"authors\":\"Itay Hazan, A. Shabtai\",\"doi\":\"10.1109/MOBILESOFT.2015.25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we attempt demonstrate how we can use smartphone sensor data effectively for predicting gender. We specifically focus on sensor data that is assumed to inflict minimal risk to other applications, the system, or the user: Installed Applications, Network Traffic Amount, and Accelerometer readings. We propose several simple heuristics for pre-processing the data and for noise reduction which eventually results in improved accuracy in predicting gender.\",\"PeriodicalId\":131706,\"journal\":{\"name\":\"2015 2nd ACM International Conference on Mobile Software Engineering and Systems\",\"volume\":\"316 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 2nd ACM International Conference on Mobile Software Engineering and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MOBILESOFT.2015.25\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 2nd ACM International Conference on Mobile Software Engineering and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MOBILESOFT.2015.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Noise Reduction of Mobile Sensors Data in the Prediction of Demographic Attributes
In this paper we attempt demonstrate how we can use smartphone sensor data effectively for predicting gender. We specifically focus on sensor data that is assumed to inflict minimal risk to other applications, the system, or the user: Installed Applications, Network Traffic Amount, and Accelerometer readings. We propose several simple heuristics for pre-processing the data and for noise reduction which eventually results in improved accuracy in predicting gender.