Single- vs. multiple-instance classification
We categorize problems by the amount of label information instances in a bag carry. We define synthetic tasks of increasing complexity or intra-bag dependency. These problems allow us to measure the power of multiple-instance algorithms. We experiment on two bioinformatics data for gene expression and text categorization.
classification bioinformatics multiple-instance learning similarity-based representation