MICCLLR: A Generalized Multiple-Instance Learning Algorithm Using Class Conditional Log Likelihood Ratio

El-Manzalawy, Yasser and Honavar, Vasant (2007) MICCLLR: A Generalized Multiple-Instance Learning Algorithm Using Class Conditional Log Likelihood Ratio. Technical Report 1, Computer Science, Iowa State University.

Abstract

We propose a new generalized multiple-instance learning (MIL) algorithm, MICCLLR (multiple-instance class conditional likelihood ratio), that converts the MI data into a single meta-instance data allowing any propositional classifier to be applied. Experimental results on a wide range of MI data sets show that MICCLLR is competitive with some of the best performing MIL algorithms reported in literature.