A Projection-Based Framework for Classifier Performance Evaluation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Visualizing Classifier Performance on Different Domains
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Classifying Biomedical Abstracts Using Committees of Classifiers and Collective Ranking Techniques
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
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The purpose of this work is to improve on the selection of algorithms for classifier committees applied to reducing the workload of human experts in building systematic reviews used in evidence-based medicine We focus on clustering pre-selected classifiers based on a multi-measure prediction performance evaluation expressed in terms of a projection from a high-dimensional space to a visualizable two-dimensional one The best classifier was selected from each cluster and included in the committee We applied the committee of classifiers to rank biomedical abstracts based on the predicted relevance to the topic under review We identified a subset of abstracts that represents the bottom of the ranked list (predicted as irrelevant) We used False Negatives (relevant articles mistakenly ranked at the bottom) as a final performance measure Our early experiments demonstrate that the classifier committee built using our new approach outperformed committees of classifiers arbitrary created from the same list of pre-selected classifiers.