A meta-learning system for multi-instance classification
Gitte Vanwinckelen, Hendrik Blockeel · Lirias · 2014
Meta-learning refers to the use of machine learning methods to analyze the behavior of machine learning methods on different types of datasets. Until now, meta-learning has mostly focused on the standard classification setting. In this paper about ongoing work, we apply it to multi-instance classification, an alternative classification setting in which bags of instances, rather than individual instances, are labeled. We define a number of data set properties that are specific to the multi-instance setting, and extend the concept of landmarkers to the multi-instance setting. Experimental results show that multi-instance classifiers are very sensitive to the context in which they are used, and that the meta-learning approach can indeed yield useful insights in this respect.