{"title":"研究一种解决非平衡数据集二值分类任务的不同方法","authors":"Dmytro Polokhach, V. Kushnir, Oleksandr Vashchuk","doi":"10.1109/aict52120.2021.9628984","DOIUrl":null,"url":null,"abstract":"This work represents methods and approaches for training artificial intelligence when dataset is unbalanced. It helps to use existing dataset instead of generating new one which can be very hard and complex. To measure such methods specific performance metrics were provided. Methods and approaches were applied and results were generated.","PeriodicalId":375013,"journal":{"name":"2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating a Different Approaches to Resolve Binary Classification Task with Unbalanced Dataset\",\"authors\":\"Dmytro Polokhach, V. Kushnir, Oleksandr Vashchuk\",\"doi\":\"10.1109/aict52120.2021.9628984\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work represents methods and approaches for training artificial intelligence when dataset is unbalanced. It helps to use existing dataset instead of generating new one which can be very hard and complex. To measure such methods specific performance metrics were provided. Methods and approaches were applied and results were generated.\",\"PeriodicalId\":375013,\"journal\":{\"name\":\"2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/aict52120.2021.9628984\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/aict52120.2021.9628984","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating a Different Approaches to Resolve Binary Classification Task with Unbalanced Dataset
This work represents methods and approaches for training artificial intelligence when dataset is unbalanced. It helps to use existing dataset instead of generating new one which can be very hard and complex. To measure such methods specific performance metrics were provided. Methods and approaches were applied and results were generated.
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