EEF: Exponentially Embedded Families with Class-Specific Features for Classification
This is an incremental improvement for classification tasks, particularly in text categorization, by allowing feature selection tailored to individual classes.
The paper tackles classification by proposing an exponentially embedded families (EEF) method that uses class-specific features instead of a common subset, and demonstrates promising performance on real-life text categorization datasets.
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.