@inproceedings{4ab8b8802087467a84804d782aa65768,
title = "Types of Errors in Personalized Spam Mail Classification",
abstract = "Most studies on the Spam Classification Models have focused on the enhancement of estimation capability assuming the correct training data set is given. However, most of training data are not really generated by the real email receivers. To explore the effects of Real Email Reviewers (RER) and variance between Third Party Reviewers (TPR), we define the types of errors caused in the personalized spam classification model. We experimented with the ham and spam mail data classified by the RER and three groups of TPRs. We found that the discrepancy is as big as 38\% between RER and TPR, and the inconsistency is potentially 56\% between TPRs. We have also tested using the LSTM model outcomes, and the result is similar. These results imply that the errors relevant to the real email reviewers should be taken into consideration when we develop a personalized AI model for spam classification.",
keywords = "Personalized, Real Email Receiver, Receiver Specific Error, Reviewer Sensitive Error, Third Party Reviewer",
author = "Fengyao Wang and Qi Huang and Shan Liu and Lee, \{Jae Kyu\} and Xinpei Dong and Yupeng Huang and Ryan Pan",
note = "Publisher Copyright: {\textcopyright} 2022, Association for Information Systems. All rights reserved.; 26th Pacific Asia Conference on Information Systems, PACIS 2022 ; Conference date: 05-07-2022 Through 09-07-2022",
year = "2022",
language = "英语",
isbn = "9781958200018",
series = "Pacific Asia Conference on Information Systems",
publisher = "Association for Information Systems",
booktitle = "Pacific Asia Conference on Information Systems, PACIS 2022",
}