MICCLLR: A Generalized Multiple-Instance Learning Algorithm Using Class Conditional Log Likelihood Ratio
Yasser EL‐Manzalawy, Vasant Honavar · Iowa State University Digital Repository (Iowa State University) · 2007
We propose a new generalized multiple-instance learn-ing (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. 1.