{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16473"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16473","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Incorporating game theory with soft sets for better decision making","abstract":"Solving uncertainty is a challenge for decision-making. Soft set theory aims to aid complex decision-making when multiple uncertainty variables are involved. To solve classification problems with the existence of uncertainty, we adopted three-way classification instead of binary classification. It introduces a boundary region to handle scenarios in which a number of objects cannot be categorized as either positive or negative with a high degree of certainty. The three-way classification problem involves multiple experts. Each expert may produce a different three-way classification outcome based on their available information and expertise. We introduced a gametheoretic soft set model to address the fusion of partial information which is available to certain experts and resolve conflicts among experts when determining the final three-way classification. It uses a soft set to represent experts and formulates a game among parameters of the soft set. The model is utilized to establish measurement thresholds for parameters. The experiment shows the model is capable of striking a balance among different parameters, resulting in a decrease in misclassification error in an environment involving uncertainty. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error. Based on the user’s specified target misclassification error and undecided error, our model can help determine an appropriate ratio.","abstract_html":"Solving uncertainty is a challenge for decision-making. Soft set theory aims to aid complex decision-making when multiple uncertainty variables are involved. To solve classification problems with the existence of uncertainty, we adopted three-way classification instead of binary classification. It introduces a boundary region to handle scenarios in which a number of objects cannot be categorized as either positive or negative with a high degree of certainty. The three-way classification problem involves multiple experts. Each expert may produce a different three-way classification outcome based on their available information and expertise. We introduced a gametheoretic soft set model to address the fusion of partial information which is available to certain experts and resolve conflicts among experts when determining the final three-way classification. It uses a soft set to represent experts and formulates a game among parameters of the soft set. The model is utilized to establish measurement thresholds for parameters. The experiment shows the model is capable of striking a balance among different parameters, resulting in a decrease in misclassification error in an environment involving uncertainty. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error. Based on the user’s specified target misclassification error and undecided error, our model can help determine an appropriate ratio.","abstract_has_math":false,"creators":["Li, Chenqi"],"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":["Hepting, Daryl"],"year":2024,"date_issued":"2024-02","date_published":"2024-02","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/10.82465/3816"],"render_values":[{"text":"https://doi.org/10.82465/3816","href":"https://doi.org/10.82465/3816","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16473","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":["Hepting, Daryl"]},{"key":"dc:creator","label":"Author","values":["Li, Chenqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-11T20:08:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-11T20:08:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-02"]},{"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/10.82465/3816"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16473"]}]},{"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. vii, 71 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Solving uncertainty is a challenge for decision-making. Soft set theory aims to aid complex decision-making when multiple uncertainty variables are involved. To solve classification problems with the existence of uncertainty, we adopted three-way classification instead of binary classification. It introduces a boundary region to handle scenarios in which a number of objects cannot be categorized as either positive or negative with a high degree of certainty. The three-way classification problem involves multiple experts. Each expert may produce a different three-way classification outcome based on their available information and expertise. We introduced a gametheoretic soft set model to address the fusion of partial information which is available to certain experts and resolve conflicts among experts when determining the final three-way classification. It uses a soft set to represent experts and formulates a game among parameters of the soft set. The model is utilized to establish measurement thresholds for parameters. The experiment shows the model is capable of striking a balance among different parameters, resulting in a decrease in misclassification error in an environment involving uncertainty. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error. Based on the user’s specified target misclassification error and undecided error, our model can help determine an appropriate ratio."]},{"key":"dc:title","label":"Title","values":["Incorporating game theory with soft sets for better decision making"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yao, JingTao"],"dc:contributor.committeemember":["Hepting, Daryl"],"dc:creator":["Li, Chenqi"],"dc:date.accessioned":["2024-10-11T20:08:09Z"],"dc:date.available":["2024-10-11T20:08:09Z"],"dc:date.issued":["2024-02"],"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. vii, 71 p."],"dc:description.abstract":["Solving uncertainty is a challenge for decision-making. Soft set theory aims to aid complex decision-making when multiple uncertainty variables are involved. 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The experiment shows the model is capable of striking a balance among different parameters, resulting in a decrease in misclassification error in an environment involving uncertainty. Furthermore, the extent of the decrease can be fine-tuned by adjusting the ratio between the cost for misclassification error and the cost for undecided error. Based on the user’s specified target misclassification error and undecided error, our model can help determine an appropriate ratio."],"dc:identifier.doi":["https://doi.org/10.82465/3816"],"dc:identifier.uri":["https://hdl.handle.net/10294/16473"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Incorporating game theory with soft sets for better decision making"],"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"}