Abstract
The goal of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset. However the relationship between data characteristics and the reducibility achieved by instance selection methods has not been extensively tested. This chapter adopts a meta-learning approach, via an empirical study of 112 classification datasets, to explore the relationship between data characteristics and the success of a naïve instance selection method. The approach can be readily extended to explore how the data characteristics influence the performance of many more sophisticated instance selection methods.
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., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Irvine (2002), http://www.ics.uci.edu/_mlearn/MLRepository.html
Liu, H., Motoda, H.: On Issues of Instance Selection. Data Mining and Knowledge Discovery 6, 115–130 (2002)
Jankowski, N., Grochowski, M.: Comparison of Instances Selection Algorithms I. Algorithms Survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)
Reinartz, T.: A Unifying View on Instance Selection. Data Mining and Knowledge Discovery 6, 191–210 (2002)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE T. Evolut. Comput. 1, 67 (1997)
Grochowski, M., Jankowski, N.: Comparison of Instance Selection Algorithms II. Results and Comments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 580–585. Springer, Heidelberg (2004)
Rice, J.R.: The Algorithm Selection Problem. Adv. Comp. 15, 65–118 (1976)
Smith-Miles, K.A.: Cross-Disciplinary Perspectives On Meta-Learning For Algorithm Selection. ACM Computing Surveys 41(1), article 6 (2008)
Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18, 77–95 (2002)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Brazdil, P., Soares, C., Costa, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Mach. Learn. 50, 251–277 (2003)
Ali, S., Smith, K.: On Learning Algorithm Selection for Classification. Appl. Soft Comp. 6, 119–138 (2006)
Prodromidis, A.L., Chan, P., Stolfo, S.J.: Meta-learning in distributed data mining systems: issues and approaches. In: Kargupta, H., Chan, P. (eds.) Advances of Distributed Data Mining. AAAI Press, Menlo Park (2000)
Bernstein, A., Provost, F., Hill, S.: Toward intelligent assistance for a data mining process: an ontology-based approach for cost-sensitive classification. IEEE Transactions on Knowledge and Data Engineering 17, 503–518 (2005)
Charest, M., Delisle, S., Cervantes, O., Shen, Y.: Bridging the gap between data mining and decision support: A case-based reasoning and ontology approach. Intelligent Data Analysis 12, 211–236 (2008)
Wilson, D.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2, 408–421 (1972)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics 6, 448–452 (1976)
Jankowski, N.: Data regularization. In: Rutkowski, L., Tadeusiewicz, R. (eds.) Neural Networks and Soft Computing, Zakopane, Poland, pp. 209–214 (2000)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14, 515–516 (1968)
Gates, G.: The reduced nearest neighbor rule. IEEE Transactions on Information Theory 18, 431–433 (1972)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6, 153–172 (2002)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38, 257–286 (2000)
Kohonen, T.: Learning Vector Quantization. Neural Networks 1, 303 (1988)
Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: International Conference on Machine Learning, pp. 293–301 (1994)
Sen, S., Knight, L.: A Genetic Prototype Learner. In: Mellish, C.S. (ed.) Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, August 20-25, vol. I, pp. 725–731. Morgan Kaufmann, San Mateo (1995)
Madigan, D., Raghavan, N., DuMouchel, W., Nason, M., Posse, C., Ridgeway, G.: Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction. Data Mining and Knowledge Discovery 6, 173–190 (2002)
Li, B., Chi, M., Fan, J., Xue, X.: Support Cluster Machine. In: 25th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (2007)
Evans, R.: Clustering for Classification: Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classification Accuracy. VDM Verlag (2008)
Li, X.: Data reduction via Adaptive Sampling. Communications in Information and Systems 2, 5–38 (2002)
Domingo, C., Gavaldà, R., Watanabe, O.: Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms. Data Mining and Knowledge Discovery 6, 131–152 (2002)
Hartigan, J.A.: Clustering Algorithms. John Wiley & Sons, Inc., New York (1975)
Marchiori, E.: Hit Miss Networks with Applications to Instance Selection. Journal of Machine Learning Research 9, 997–1017 (2008)
Ali, S., Smith-Miles, K.A.: A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 70, 173–186 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Hathaway, R.J., Bezdek, J.C.: Visual cluster validity for prototype generator clustering models. Pattern Recognition Letters 24, 1563–1569 (2003)
Pfahringer, B., Bensusan, H., Giraud-Carrier, C.G.: Meta-Learning by Landmarking Various Learning Algorithms. In: Proc. ICML, pp. 74–750 (2000)
Smith, K.A.: Neural Networks for Prediction and Classification. In: Wang, J(ed.), Encyclopaedia of Data Warehousing And Mining, vol. 2, pp. 864–869. Information Science Publishing, Hershey PA (2006)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biol. Cyber. 43, 59–69 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Smith-Miles, K.A., Islam, R.M.D. (2011). Meta-Learning of Instance Selection for Data Summarization. In: Jankowski, N., Duch, W., Gra̧bczewski, K. (eds) Meta-Learning in Computational Intelligence. Studies in Computational Intelligence, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20980-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-20980-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20979-6
Online ISBN: 978-3-642-20980-2
eBook Packages: EngineeringEngineering (R0)