Abstract
In this paper, a decision cluster forest classification model is proposed for high dimensional data with multiple classes. A decision cluster forest (DCF) consists of a set of decision cluster trees, in which the leaves of each tree are clusters labeled with the same class that determines the class of new objects falling in the clusters. By recursively calling a variable weighting k-means algorithm, a decision cluster tree can be generated from a subset of the training data that contains the objects in the same class. The set of m decision cluster trees grown from the subsets of m classes constitute the decision cluster forest. Anderson-Darling test is used to determine the stopping condition of tree growing. A DCF classification (DCFC) model is selected from all leaves of the m decision cluster trees in the forest. A series of experiments on both synthetic and real data sets have shown that the DCFC model performed better in accuracy and scalability than the single decision cluster tree method and the methods of k-NN, decision tree and SVM. This new model is particularly suitable for large, high dimensional data with many classes.
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
Piatetsky-Shapiro, G., Djeraba, C., Getoor, L., Grossman, R., Feldman, R., Zaki, M.: What are the grand challenges for data mining? In: KDD 2006 panel report. SIGKDD Explorations, vol. 8, pp. 70–77 (2006)
Li, Y., Hung, E., Chung, K., Huang, J.: Building a decision cluster classification model by a variable weighting k-means method. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 337–347. Springer, Heidelberg (2008)
Huang, J., Ng, M., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 657–668 (2005)
Jing, L., Huang, J., Ng, M.K., Rong, H.: A feature weighting approach to building classification models by interactive clustering. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 284–294. Springer, Heidelberg (2004)
Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Anderson, T.W., Darling, D.A.: Asymptotic theory of certain ”goodness-of-fit” criteria based on stochastic processes. The Annals of Mathematical Statistics 23, 193–212 (1952)
Stephens, M.A.: Edf statistics for goodness of fit and some comparisons. Journal of the American Statistical Association 69, 730–737 (1974)
Kyriakopoulou, A., Kalamboukis, T.: Text classification using clustering. In: ECML-PKDD Discovery Challenge Workshop Proceedings (2006)
Zhang, B., Srihari, S.N.: Fast k-nearest neighbor classification using cluster-based trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 525–528 (2004)
Mui, J., Fu, K.: Automated classification of nucleated blood cells using a binary tree classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 429–443 (1980)
Huang, Z., Ng, M., Lin, T., Cheung, D.: An interactive approach to building classification models by clustering and cluster validation. In: Leung, K.-S., Chan, L., Meng, H. (eds.) IDEAL 2000. LNCS, vol. 1983, pp. 23–28. Springer, Heidelberg (2000)
Huang, Z., Lin, T.: A visual method of cluster validation with fastmap. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 153–164. Springer, Heidelberg (2000)
Blockeel, H., Raedt, L., Ramong, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55–63 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Hung, E. (2009). Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-05224-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05223-1
Online ISBN: 978-3-642-05224-8
eBook Packages: Computer ScienceComputer Science (R0)