# EEF: Exponentially Embedded Families with Class-Specific Features for Classification

arXiv: https://arxiv.org/abs/1605.03631 · HTML: https://mlsanity.com/paper/1605.03631/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2016.

## Built on by (most cited first, 2 of 2)

- [XNB: Explainable Class-Specific NaIve-Bayes Classifier](https://mlsanity.com/paper/2411.01203/) (2024)
- [Class-specific feature selection for classification explainability](https://mlsanity.com/paper/2411.01204/) (2024)

## Abstract (arXiv)

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain (IG) score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.