Conical Classification For Computationally Efficient One-Class Topic Determination

• URL: http://arxiv.org/abs/2111.00375v1
• Date: Sun, 31 Oct 2021 01:27:12 GMT
• Title: Conical Classification For Computationally Efficient One-Class Topic Determination
• Authors: Sameer Khanna
• Abstract summary: We propose a Conical classification approach to identify documents that relate to a particular topic. We show in our analysis that our approach has higher predictive power on our datasets, and is also faster to compute.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.

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