Conical Classification For Computationally Efficient One-Class Topic Determination

TL;DR

This paper introduces Conical classification, a computationally efficient method for one-class topic detection in large text datasets, improving predictive power and speed over existing methods.

Contribution

The paper proposes Conical classification and Normal Exclusion, novel techniques that enhance efficiency and accuracy in one-class text classification tasks.

Findings

Higher predictive accuracy on tested datasets

Faster computation compared to existing methods

Effective identification of topic-specific documents

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.