# Conical Classification For Computationally Efficient One-Class Topic Determination

arXiv: https://arxiv.org/abs/2111.00375 · HTML: https://mlsanity.com/paper/2111.00375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 68 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams](https://mlsanity.com/paper/1710.00811/) (2017)
- [A Comparison of Semantic Similarity Methods for Maximum Human Interpretability](https://mlsanity.com/paper/1910.09129/) (2019)
- [Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network (RNN)](https://mlsanity.com/paper/1805.03687/) (2018)
- [Towards objectively evaluating the quality of generated medical summaries](https://mlsanity.com/paper/2104.04412/) (2021)
- [One Class Splitting Criteria for Random Forests](https://mlsanity.com/paper/1611.01971/) (2016)
- [Image-Based Feature Representation for Insider Threat Classification](https://mlsanity.com/paper/1911.05879/) (2019)
- [Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives](https://mlsanity.com/paper/2104.03934/) (2021)

## Abstract (arXiv)

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.