{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/8893"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/8893","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Incorporating Three-Way Email Spam Filtering With Game-Theoretic Rough Sets","abstract":"Email spam filtering commonly is viewed as binary classification problem, that is, classifies incoming email messages into spam or non-spam email. But it has two main limitations. Firstly, binary classification needs people to make definite decisions that are hard. Secondly, binary classification leads to high misclassification rate. Decreasing rate of misclassifying legitimate email to spam may cause the increasing rate of misclassifying spam to legitimate email. Three-way email spam filtering can reduce the misclassification by dividing incoming email messages into three folders, i.e., a spam folder containing email messages which are junk, an inbox or a legitimate folder containing email messages which are readable, and a suspected folder containing email messages which are not sure to make decisions based on available information. Three-way email spam filtering is result from the three-way classifications. Rough set theory is a theory that is used in the field of data analysis. Rough set theory provides a way to make decisions in incomplete and insufficient knowledge. Three-way classifications are constructed on the concept of rough set theory. This means that three-way classifications are constructed based on acceptance, rejection, and deferment. The decisions are induced from acceptance and rejection regions. The decisions are not made in deferment regions until additional information is collected. The rough sets are too restrict to tolerate classification errors in the positive (acceptance), and negative (rejection) regions. Probabilistic rough sets relax the requirement. Probabilistic rough sets allow for the more objects contained in the i positive and negative regions. Probabilistic rough sets cannot determine the boundaries of the positive, negative, and the boundary regions. In other words, how to determine the thresholds is the key. Game-theoretic rough sets (GTRS) determined three-way classifications from tradeoff perspective when multiple criteria are involved to evaluate the three-way classification model. GTRS provide games to obtain a tradeoff between the criteria. The balanced thresholds of three-way classifications can be induced from the equilibrium of games. In this thesis, GTRS are applied into three-way email spam filtering to obtain a suitable tradeoff between accuracy and coverage. The competitive games are formu- lated between accuracy and coverage to obtain probabilistic thresholds of three-way classifications. Experimental results on the Spambase dataset show that three-way email spam filtering based on GTRS is able to improve the coverage level in contrast to Pawlak rough sets and 0.5-probabilistic model. It is hoped that this thesis would lead to the understanding of GTRS and three- way email spam filtering.","abstract_html":"Email spam filtering commonly is viewed as binary classification problem, that is, classifies incoming email messages into spam or non-spam email. But it has two main limitations. Firstly, binary classification needs people to make definite decisions that are hard. Secondly, binary classification leads to high misclassification rate. Decreasing rate of misclassifying legitimate email to spam may cause the increasing rate of misclassifying spam to legitimate email. Three-way email spam filtering can reduce the misclassification by dividing incoming email messages into three folders, i.e., a spam folder containing email messages which are junk, an inbox or a legitimate folder containing email messages which are readable, and a suspected folder containing email messages which are not sure to make decisions based on available information. Three-way email spam filtering is result from the three-way classifications. Rough set theory is a theory that is used in the field of data analysis. Rough set theory provides a way to make decisions in incomplete and insufficient knowledge. Three-way classifications are constructed on the concept of rough set theory. This means that three-way classifications are constructed based on acceptance, rejection, and deferment. The decisions are induced from acceptance and rejection regions. The decisions are not made in deferment regions until additional information is collected. The rough sets are too restrict to tolerate classification errors in the positive (acceptance), and negative (rejection) regions. Probabilistic rough sets relax the requirement. Probabilistic rough sets allow for the more objects contained in the i positive and negative regions. Probabilistic rough sets cannot determine the boundaries of the positive, negative, and the boundary regions. In other words, how to determine the thresholds is the key. Game-theoretic rough sets (GTRS) determined three-way classifications from tradeoff perspective when multiple criteria are involved to evaluate the three-way classification model. GTRS provide games to obtain a tradeoff between the criteria. The balanced thresholds of three-way classifications can be induced from the equilibrium of games. In this thesis, GTRS are applied into three-way email spam filtering to obtain a suitable tradeoff between accuracy and coverage. The competitive games are formu- lated between accuracy and coverage to obtain probabilistic thresholds of three-way classifications. Experimental results on the Spambase dataset show that three-way email spam filtering based on GTRS is able to improve the coverage level in contrast to Pawlak rough sets and 0.5-probabilistic model. It is hoped that this thesis would lead to the understanding of GTRS and three- way email spam filtering.","abstract_has_math":false,"creators":["Liu, Pengfei"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Science (MSc)","degree_level":"Master's","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Yao, JingTao"],"committee_chairs":[],"committee_members":["Fan, Lisa","Yang, Boting"],"year":2019,"date_issued":"2019-02","date_published":"2019-02","updated_at":"2026-07-24T04:03:27Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3847"],"render_values":[{"text":"https://doi.org/10.82465/3847","href":"https://doi.org/10.82465/3847","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/8893","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Yao, JingTao"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Fan, Lisa","Yang, Boting"]},{"key":"dc:creator","label":"Author","values":["Liu, Pengfei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-06-21T20:06:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-21T20:06:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-02"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3847"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/8893"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. viii, 60 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Email spam filtering commonly is viewed as binary classification problem, that is, classifies incoming email messages into spam or non-spam email. But it has two main limitations. Firstly, binary classification needs people to make definite decisions that are hard. Secondly, binary classification leads to high misclassification rate. Decreasing rate of misclassifying legitimate email to spam may cause the increasing rate of misclassifying spam to legitimate email. Three-way email spam filtering can reduce the misclassification by dividing incoming email messages into three folders, i.e., a spam folder containing email messages which are junk, an inbox or a legitimate folder containing email messages which are readable, and a suspected folder containing email messages which are not sure to make decisions based on available information. Three-way email spam filtering is result from the three-way classifications. Rough set theory is a theory that is used in the field of data analysis. Rough set theory provides a way to make decisions in incomplete and insufficient knowledge. Three-way classifications are constructed on the concept of rough set theory. This means that three-way classifications are constructed based on acceptance, rejection, and deferment. The decisions are induced from acceptance and rejection regions. The decisions are not made in deferment regions until additional information is collected. The rough sets are too restrict to tolerate classification errors in the positive (acceptance), and negative (rejection) regions. Probabilistic rough sets relax the requirement. Probabilistic rough sets allow for the more objects contained in the i positive and negative regions. Probabilistic rough sets cannot determine the boundaries of the positive, negative, and the boundary regions. In other words, how to determine the thresholds is the key. Game-theoretic rough sets (GTRS) determined three-way classifications from tradeoff perspective when multiple criteria are involved to evaluate the three-way classification model. GTRS provide games to obtain a tradeoff between the criteria. The balanced thresholds of three-way classifications can be induced from the equilibrium of games. In this thesis, GTRS are applied into three-way email spam filtering to obtain a suitable tradeoff between accuracy and coverage. The competitive games are formu- lated between accuracy and coverage to obtain probabilistic thresholds of three-way classifications. Experimental results on the Spambase dataset show that three-way email spam filtering based on GTRS is able to improve the coverage level in contrast to Pawlak rough sets and 0.5-probabilistic model. It is hoped that this thesis would lead to the understanding of GTRS and three- way email spam filtering."]},{"key":"dc:title","label":"Title","values":["Incorporating Three-Way Email Spam Filtering With Game-Theoretic Rough Sets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yao, JingTao"],"dc:contributor.committeemember":["Fan, Lisa","Yang, Boting"],"dc:creator":["Liu, Pengfei"],"dc:date.accessioned":["2019-06-21T20:06:49Z"],"dc:date.available":["2019-06-21T20:06:49Z"],"dc:date.issued":["2019-02"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina. viii, 60 p."],"dc:description.abstract":["Email spam filtering commonly is viewed as binary classification problem, that is, classifies incoming email messages into spam or non-spam email. But it has two main limitations. Firstly, binary classification needs people to make definite decisions that are hard. Secondly, binary classification leads to high misclassification rate. Decreasing rate of misclassifying legitimate email to spam may cause the increasing rate of misclassifying spam to legitimate email. Three-way email spam filtering can reduce the misclassification by dividing incoming email messages into three folders, i.e., a spam folder containing email messages which are junk, an inbox or a legitimate folder containing email messages which are readable, and a suspected folder containing email messages which are not sure to make decisions based on available information. Three-way email spam filtering is result from the three-way classifications. Rough set theory is a theory that is used in the field of data analysis. Rough set theory provides a way to make decisions in incomplete and insufficient knowledge. Three-way classifications are constructed on the concept of rough set theory. This means that three-way classifications are constructed based on acceptance, rejection, and deferment. The decisions are induced from acceptance and rejection regions. The decisions are not made in deferment regions until additional information is collected. The rough sets are too restrict to tolerate classification errors in the positive (acceptance), and negative (rejection) regions. Probabilistic rough sets relax the requirement. Probabilistic rough sets allow for the more objects contained in the i positive and negative regions. Probabilistic rough sets cannot determine the boundaries of the positive, negative, and the boundary regions. In other words, how to determine the thresholds is the key. Game-theoretic rough sets (GTRS) determined three-way classifications from tradeoff perspective when multiple criteria are involved to evaluate the three-way classification model. GTRS provide games to obtain a tradeoff between the criteria. The balanced thresholds of three-way classifications can be induced from the equilibrium of games. In this thesis, GTRS are applied into three-way email spam filtering to obtain a suitable tradeoff between accuracy and coverage. The competitive games are formu- lated between accuracy and coverage to obtain probabilistic thresholds of three-way classifications. Experimental results on the Spambase dataset show that three-way email spam filtering based on GTRS is able to improve the coverage level in contrast to Pawlak rough sets and 0.5-probabilistic model. It is hoped that this thesis would lead to the understanding of GTRS and three- way email spam filtering."],"dc:identifier.doi":["https://doi.org/10.82465/3847"],"dc:identifier.uri":["https://hdl.handle.net/10294/8893"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Incorporating Three-Way Email Spam Filtering With Game-Theoretic Rough Sets"],"dc:type":["master thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:27Z"}