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

TL;DR

This paper introduces a novel classification method called EEF that estimates data PDFs from feature PDFs, enabling class-specific feature use, and demonstrates its effectiveness in text categorization.

Contribution

The paper proposes a new EEF-based classifier that incorporates class-specific features estimated from data PDFs, advancing beyond traditional common feature subsets.

Findings

Effective in text categorization tasks

Outperforms conventional classifiers on real datasets

Shows potential for wide application

Abstract

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.