{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/13775"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/13775","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"ENSEMBLE-BASED OUTLIER DETECTION","abstract":"Outlier detection is the process of detecting observations that do not conform to the norm of the dataset. Outlier detection is an unsupervised and thus ill-posed problem. This makes the outlier detection task particularly challenging. Ensembles have the potential to address the ill-posed nature of outlier detection. Ensemble techniques have proven to be effective for classification and clustering task yet anomaly ensembles have only recently been studied. In this dissertation, I present strategies to develop robust and accurate ensemble methods including neural network-based approaches for detecting outliers. Specifically, I introduce (i) Soul, a framework for building selective outlier ensembles that has shown potential for minimizing the negative impact of poor components in an ensemble, (ii) BAE, an unsupervised boosting-based ensemble approach that is proposed to overcome limitations of using autoencoders in outlier detection, and (iii) \\ExVAE, an exemplar-based variational autoencoder with a novel application in the field of outlier detection, tailor-made to tackle the challenges posed in this domain.","abstract_html":"Outlier detection is the process of detecting observations that do not conform to the norm of the dataset. Outlier detection is an unsupervised and thus ill-posed problem. This makes the outlier detection task particularly challenging. Ensembles have the potential to address the ill-posed nature of outlier detection. Ensemble techniques have proven to be effective for classification and clustering task yet anomaly ensembles have only recently been studied. In this dissertation, I present strategies to develop robust and accurate ensemble methods including neural network-based approaches for detecting outliers. Specifically, I introduce (i) Soul, a framework for building selective outlier ensembles that has shown potential for minimizing the negative impact of poor components in an ensemble, (ii) BAE, an unsupervised boosting-based ensemble approach that is proposed to overcome limitations of using autoencoders in outlier detection, and (iii) \\ExVAE, an exemplar-based variational autoencoder with a novel application in the field of outlier detection, tailor-made to tackle the challenges posed in this domain.","abstract_has_math":false,"creators":["Sarvari, Hamed"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-27T19:52:10Z","subjects":["Deep learning","Ensemble methods","Machine learning","Outlier detection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/13775"],"render_values":[{"text":"hdl:1920/13775","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","Ensemble methods","Machine learning","Outlier detection"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/13775"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Outlier detection is the process of detecting observations that do not conform to the norm of the dataset. Outlier detection is an unsupervised and thus ill-posed problem. This makes the outlier detection task particularly challenging. Ensembles have the potential to address the ill-posed nature of outlier detection. Ensemble techniques have proven to be effective for classification and clustering task yet anomaly ensembles have only recently been studied. In this dissertation, I present strategies to develop robust and accurate ensemble methods including neural network-based approaches for detecting outliers. Specifically, I introduce (i) Soul, a framework for building selective outlier ensembles that has shown potential for minimizing the negative impact of poor components in an ensemble, (ii) BAE, an unsupervised boosting-based ensemble approach that is proposed to overcome limitations of using autoencoders in outlier detection, and (iii) \\ExVAE, an exemplar-based variational autoencoder with a novel application in the field of outlier detection, tailor-made to tackle the challenges posed in this domain."]},{"key":"dc:title","label":"Title","values":["ENSEMBLE-BASED OUTLIER DETECTION"]}]}],"canonical_facts":{"dc:date.issued":["2023"],"dc:description.other":["Outlier detection is the process of detecting observations that do not conform to the norm of the dataset. Outlier detection is an unsupervised and thus ill-posed problem. This makes the outlier detection task particularly challenging. Ensembles have the potential to address the ill-posed nature of outlier detection. Ensemble techniques have proven to be effective for classification and clustering task yet anomaly ensembles have only recently been studied. In this dissertation, I present strategies to develop robust and accurate ensemble methods including neural network-based approaches for detecting outliers. Specifically, I introduce (i) Soul, a framework for building selective outlier ensembles that has shown potential for minimizing the negative impact of poor components in an ensemble, (ii) BAE, an unsupervised boosting-based ensemble approach that is proposed to overcome limitations of using autoencoders in outlier detection, and (iii) \\ExVAE, an exemplar-based variational autoencoder with a novel application in the field of outlier detection, tailor-made to tackle the challenges posed in this domain."],"dc:identifier":["hdl:1920/13775"],"dc:subject":["Deep learning","Ensemble methods","Machine learning","Outlier detection"],"dc:title":["ENSEMBLE-BASED OUTLIER DETECTION"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:52:10Z"}