Abstract
Many classifiers achieve high levels of accuracy but have limited use in real world problems because they provide little insight into data sets, are difficult to interpret and require expertise to use. In areas such as health informatics not only do analysts require accurate classifications but they also want some insight into the influences on the classification. This can then be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that gives accuracy comparable to other techniques whist providing insight into the data. AWSum achieves this by calculating a weight for each feature value that represents its influence on the class value. The merits of AWSum in classification and insight are tested on a Cystic Fibrosis dataset with positive results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blake, C.L., Newman, D.J., Hettich, S., Merz, C.J.: UCI repository of machine learning databases (1988)
Duda, R., Hart, P.: Pattern Classification and scene analysis. John Wiley, Chichester (1973)
Friedmann, N., Goldszmidt: Building classifiers using bayesian intelligence. In: Proceedings of the National Conference on Artificial Intelligence, pp. 207–216. AAAI Press, Portland (1993)
Quinlan, J.: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Quinn, A., Stranieri, A., Yearwood, J.: Classification for accuracy and insight. A weighted sum approach. In: Proceedings of 6th Austalasian data mining conference, Gold Coast, Australia, vol. 70 (2007)
Setiono, R., Liu, H.: Symbolic Representation of Neural Networks, Computer, vol. 29, pp. 71–77. IEEE Computer Society Press, Los Alamitos (1973)
Shafer, G.: A Mathematical theory of evidence. Princeton University Press, Princeton (1993)
Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1999)
Witten, I.H., Frank, E.: Data Mining: Practicle machine learning tools and techniques with java implementations. Morgan Kaufmann, San Francisco (2000)
Klotz, S., Nand, K., Richard De Armond, R., Donald Sheppard, D., Khardori, N., Edwards Jr., J.E., Lipkee, P.N., El-Azizi, M.: Candida albicans Als proteins mediate aggregation with bacteria and yeasts Medical Mycology, vol. 45, pp. 363–370 (2007)
Australian Cystic Fibrosis Data Registry: Cystic Fibrosis database (1999–2003)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, pp. 1022–1027 (1993)
Maiz, M., Cuevas, M., Lamas, A., Sousa, A., Santiago, Q., Saurez, S.: Aspergillus fumigatus and Candia Albicans in Cystic Fibrosis: Clinical Significance and Specific Immune Response Involving Serum Immunoglobulins G, A and M. Arch Bronconeumol, vol. 44(3), pp. 146–151 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Quinn, A., Stranieri, A., Yearwood, J., Hafen, G. (2008). AWSum – Data Mining for Insight. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-88192-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
eBook Packages: Computer ScienceComputer Science (R0)