Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sackett, D., Rosenberg, W., Gray, J., Haynes, R., Richardson, W.: Evidence based medicine: what it is and what it isn’t. BMJ 312 (7023): 71-2. PMID 8555924 (1996)
Kouznetsov, A., Matwin, S., Inkpen, D., Razavi, A., Frunza, O., Sehatkar, M., Seaward, L., O’Blenis, P.: Classifying Biomedical Abstracts Using Committees of Classifiers and Collective Ranking Techniques. In: Canadian Artificial Intelligence Conference (2009)
Alaiz-Rodriguez, R., Japkowicz, N., Tischer, P.: Visualizing Classifier Performance. In: Proceedings of the 20th IEEE International Conference on Tools for Artificial Intelligence, ICTAI 2008 (2008)
Alaiz-Rodriguez, R., Japkowicz, N., Tischer, P.: A Visualization-Based Exploratory Tool for Classifier Comparison with respect to Multiple Metrics and Multiple Domains. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 660–665. Springer, Heidelberg (2008)
Japkowicz, N., Sanghi, P., Tischer, P.: A Projection-Based Framework for Classifier Performance Evaluation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 548–563. Springer, Heidelberg (2008)
Razavi, A.H., Matwin, S., Inkpen, D., Kouznetsov, A.: Parameterized Contrast in Second Order Soft Co-Occurrences: A Novel Text Representation Technique in Text Mining and Knowledge Extraction. In: Second International Workshop on Semantic Aspects in Data Mining (SADM 2009), USA, Miami (2009)
Software package Weka, http://www.cs.waikato.ac.nz/ml/weka/
Cox, T., Cox, M.: Multidimensional Scaling. Chapman and Hall, Boca Raton (October 1994)
Visualization Software for Clasifier Evaluation, http://www.site.uottawa.ca/~nat/Visualization_Software/visualization.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kouznetsov, A., Japkowicz, N. (2010). Using Classifier Performance Visualization to Improve Collective Ranking Techniques for Biomedical Abstracts Classification. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-13059-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
eBook Packages: Computer ScienceComputer Science (R0)