In this paper, we present an approach to selecting models for novelty (outlier) detection. Our approach minimizes the risk of accepting outliers at a fixed normal rejection
\nrate, under the assumption that the distribution of abnormal (outlier) data is uniformly distributed in some bounded region of the input space. This risk is minimized
\nby selecting the model with the smallest volume acceptance region, using a random-
\nized volume estimation algorithm. The volume estimation algorithm can estimate
\nthe volume of a body in high-dimensional space and scales polynomially in dimension
\nwith the number of calls to the model. We have performed extensive experiments
\nwhich show that the combined model selection criteria are able to select not only the
\nbest models from a given model class, but also among all model classes.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present an approach to selecting models for novelty (outlier) detection. Our approach minimizes the risk of accepting outliers at a fixed normal rejection rate, under the assumption that the distribution of abnormal (outlier) data is uniformly distributed in some bounded region of the input space. This risk is minimized by selecting […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-443826","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"Department of Computer Science, University of Toronto","msr_edition":"UTML TR 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