Abstract
Meta-learning is one of subsections of supervised machine learning that has continuously grown with interests to apply on new data sets in the late years. Meta learning is the process of knowledge that is acquired by the examples. Bagging, dagging, decorate, rotation forest, and filtered classifiers are well known meta-learning algorithms that are performed to compare with these meta-learning algorithms on 8 different biomedical datasets. In these algorithms, the rotation forest had the better results according to F-measurement and ROC Area in most cases.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Maudsley, D.B.: A Theory of Meta-Learning and Principles of Facilitation: An Organismic Perspective. University of Toronto (1979). 40, 8, 4354-4355-A
Muresan, S.: Pre-processing flow for enhancing learning from medical data. In: Int. Computer Comm. and Processing (ICCP), pp. 27–34 (2015)
El-Bialy, R., Salamay, M.A., Karam, O.H., Khalifa, M.E.: Feature Analysis of Coronary Artery Heart Disease Data Sets. Procedia Computer Science 65, 459–468 (2015)
Arredondo, T., Ormazabal, W.: Meta-learning framework applied in bioinformatics inference system design. Int. J. Data Min. Bioinform. 11(2), 139–166 (2015)
Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2013). http://archive.ics.uci.edu/ml
Guvenir, H.A., Acar, B., Demiroz, G., Cekin, A.: A supervised machine learning algorithm for arrhythmia analysis. In: Proceedings of the Comp. in Cardiology Conference, vol. 24, pp. 433–436 (1997)
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., Froelicher, V.: International application of a new probability algorithm for the diagnosis of coronary artery disease. A. Journal of Cardiology 64, 304–310 (1989)
RochaNeto, A.R., Barreto, G.A.: On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis. IEEE Latin America Transactions 7(4), 487–496 (2009)
de Campos, A., et al.: SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J. Matern. Fetal Med. 5, 311–318 (2000)
Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Symposium on Computer Applications and Medical Care, pp. 261–265 (1988)
Elter, M., Schulz-Wendtland, R., Wittenberg, T.: The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Medical Physics 34(11), 4164–4172 (2007)
McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection. Biomedical Engineering OnLine 6, 23 (2007)
William, H.W., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In: Proceedings of the National Academy of Sciences, USA, vol. 87, 9193–9196, December 1990
Breiman L.: Bias, variance and arcing classifiers. Technical Report TR. 400 (1996)
Nithya, R., Manikandan, P., Ramyachitra, D.: Performance Analysis of Meta Classifiers Algorithms using Yeast Dataset. Int. J. of Innovative Research in Comp. and Com. Eng. 3(9), 8062–8068 (2015)
Kotsianti, S.B., Kanellopoulos, D.: Combining bagging, boosting and dagging for classification problems. In: Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, vol. 4693, pp. 493–500 (2007)
Melville, P., Mooney, R.J.: Constructing diverse classifier ensembles using artificial training examples. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 505–510 (2003)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analyses and Machine Intelligence 28(10), 1619–1630 (2006)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
Powers, D.M.: Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies 2(1), 37–63 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Ibrikci, T., Karabulut, E.M., Uwisengeyimana, J.D. (2016). Meta Learning on Small Biomedical Datasets. In: Kim, K., Joukov, N. (eds) Information Science and Applications (ICISA) 2016. Lecture Notes in Electrical Engineering, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-10-0557-2_89
Download citation
DOI: https://doi.org/10.1007/978-981-10-0557-2_89
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0556-5
Online ISBN: 978-981-10-0557-2
eBook Packages: EngineeringEngineering (R0)