{"title":"Payment Type Classification on Urban Taxi Big Data using Deep Learning Neural Network","authors":"H. Al-Ash, A. Wibisono, A. Krisnadhi","doi":"10.1109/ICACSIS.2018.8618200","DOIUrl":null,"url":null,"abstract":"Taxi service as a reliable means of public transportation is a public need. Classification of payment types is performed on New York City Yellow Taxi Trip Open Data that considered as big data and there is a number of unlabelled data greater than the number of labeled training data was situated. We used the framework namely learning from unlabelled data (lfun) and deep learning neural network as the classifier to address the classification problem. Experimentation to find out the better performance of using lfun was conducted. We achieved the f-measure average value reaching 0.725 for classification using the lfun framework.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACSIS.2018.8618200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Taxi service as a reliable means of public transportation is a public need. Classification of payment types is performed on New York City Yellow Taxi Trip Open Data that considered as big data and there is a number of unlabelled data greater than the number of labeled training data was situated. We used the framework namely learning from unlabelled data (lfun) and deep learning neural network as the classifier to address the classification problem. Experimentation to find out the better performance of using lfun was conducted. We achieved the f-measure average value reaching 0.725 for classification using the lfun framework.
的士服务作为一种可靠的公共交通工具是市民的需要。在纽约市黄色出租车旅行开放数据(New York City Yellow Taxi Trip Open Data)上进行支付类型分类,该数据被认为是大数据,未标记的数据数量大于已标记的训练数据数量。我们使用框架即从未标记数据中学习(lfun)和深度学习神经网络作为分类器来解决分类问题。为了找出使用lfun的更好的性能,进行了实验。我们使用lfun框架实现了分类的f测量平均值达到0.725。