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the meta-data set or deriving new and more relevant
features (Niculescu-Mizil, 2009). We attempt to
improve the Above Mean neighbor selection
strategy, by computing and constantly updating a
mean distance between every two datasets in our
database. Limiting the neighbor selection strategy as
the number of problems in the system increases is
another present concern. We also want to improve
the system by generating a “best-possible” model.
For this we intend to use different classifiers, each
classifier optimized to increase the true positive rate
on its class, thus maximizing the prediction power of
the model.
ACKNOWLEDGEMENTS
Research described in this paper was supported by
the IBM Faculty Award received in 2009 by Rodica
Potolea from the Computer Science Department of
the Faculty of Computer Science, Technical
University of Cluj-Napoca, Romania.
REFERENCES
Aha D. W., 1992, Generalizing from Case Studies: A Case
Study, Proceedings of the Ninth International
Conference on Machine Learning, pp. 1 -10
Bensusan H., Giraud-Carrier C., Kennedy C. J., 2000, A
Higher-Order Approach to Meta-Learning,
proceedings of the ECML-2000 Workshop on Meta-
Learning: Building Automatic Advice Strategies for
Model Selection and Method Combination, pp. 33-42
Brazdil P. B., Soares C., Da Costa J. P., 2003, Ranking
Learning Algorithms: Using IBL and Meta-Learning
on Accuracy and Time Results, Machine Learning 50,
pp. 251-277
Cacoveanu S., Vidrighin C., Potolea R., 2009, Evolutional
Meta-Learning Framework for Automatic Classifier
Selection, proceedings of the 5th International
Conference on Intelligent Computer Communication
and Processing, Cluj-Napoca, pp. 27-30
Caruana R., Niculescu-Mizil A., 2004, Data Mining in
Metric Space: An Empirical Analysis of Supervised
Learning Performance Criteria, Proceedings of the
Tenth ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, pp. 69-78
Giraud-Carrier C., Bensusan H., 2000, Discovering Task
Neighbourhoods Through Landmark Learning
Performances, Proceedings of the Fourth European
Conference of Principles and Practice of Knowledge
Discovery in Databases, pp. 325-330
Japkowicz N., Stephen S., 2002, The Class Imbalance
Problem: A Systematic Study, Intelligent Data
Analysis, Volume 6, Number 5, pp. 429-450
Kalousis A., 2002, Algorithm Selection via
Meta_Learning, PhD Thesis, Faculte des sciences de
l’Universite de Geneve
Linder C., Studer R., 1999, Support for Algorithm
Selection with a CBR Approach, Proceedings of the
16th International Conference on Machine Learning,
pp. 418-423
Michie D., Spiegelhalter D. J., Taylor C. C., 1994,
Machine Learning. Neural and Statistical
Classification, Ellis Horwood Series in Artificial
Intelligence
Niculescu-Mizil A., et al, 2009, Winning the KDD Cup
Orange Challenge with Ensemble Selection, JMLR
Workshop and Conference Proceedings 7
Rendel L., Seshu R., Tcheng D., 1987, Layered concept
learning and dynamically variable bias management,
10th Internatinal Joint Conf. on AI, pp. 308-314
Schaffner C., 1993, Selecting a classification method by
cross validation, Machine Learning 13, pp. 135-143
Sleeman D., Rissakis M., Craw S., Graner N., Sharma S.,
1995, Consultant-2: Pre and post-processing of
machine learning applications, International Journal
of Human Computer Studies, pp. 43-63
UCI Machine Learning Data Repository,
http://archive.ics.uci.edu/ml/, last accessed Jan. 2010
Vilalta R., Giraud-Carrier C., Brazdil P., Soares C., 2004,
Using Meta-Learning to Support Data Mining,
Iternational Journal of Computer Science &
Applications, pp. 31-45
Witten I. H., Frank E., 2005, Data Mining: Practical
Machine Learning Tools and Techniques, 2nd edition,
Morgan Kaufmann Publishers, Elsevier Inc.
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