A fairly recent development in the WEKA software has been the addition of algorithms for multi-instance classification, in particular, methods for ensemble learning. Ensemble classification is a well-known approach for obtaining highly accurate classifiers for single-instance data. This talk will first discuss how randomisation can be applied to multi-instance data by adapting Blockeel et al.'s multi-instance tree inducer to form an ensemble classifier, and then investigate how Maron's diverse density learning method can be used as a weak classifier to form an ensemble using boosting. Experimental results show the benefit of ensemble learning in both cases.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Experiments with Randomisation and Boosting for Multi-instance Classification
1. Experiments with Randomisation
and Boosting for Multi-instance
Classification
Luke Bjerring, James Foulds, Eibe Frank
University of Waikato
September 13, 2011