EEF: Exponentially Embedded Families with Class-Specific Features for Classification
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.
Cite
@article{arxiv.1605.03631,
title = {EEF: Exponentially Embedded Families with Class-Specific Features for Classification},
author = {Bo Tang and Steven Kay and Haibo He and Paul M. Baggenstoss},
journal= {arXiv preprint arXiv:1605.03631},
year = {2016}
}
Comments
9 pages, 3 figures, to be published in IEEE Signal Processing Letter. IEEE Signal Processing Letter, 2016