Abstract
Biological data presents unique problems for data analysis due to its high dimensions. Microarray data is one example of such data which has received much attention in recent years. Machine learning algorithms such as support vector machines (SVM) are ideal for microarray data due to its high classification accuracies. However, sometimes the information being sought is a list of genes which best separates the classes, and not a classification rate.
Decision trees are one alternative which do not perform as well as SVMs, but their output is easily understood by non-specialists. A major obstacle with applying current decision tree implementations for high-dimensional data sets is their tendency to assign the same scores for multiple attributes. In this paper, we propose two distribution-dependant criteria for decision trees to improve their usefulness for microarray classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Quinlan, J.R.: Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996), Source available from: http://www.rulequest.com/Personal/
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques with Java implementations, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)
Luo, R.C., Scherp, R.S., Lanzo, M.: Object identification using automated decision tree construction approach for robotics applications. Journal of Robotic Systems 4(3), 423–433 (1987)
Shang, N., Breiman, L.: Distribution based trees are more accurate. In: Proc. International Conference on Neural Information Processing, pp. 133–138 (1996)
Loh, W.Y., Shih, Y.S.: Split selection methods for classification trees. Statistica Sinica 7, 815–840 (1997)
Alon, U., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. National Academy of Sciences USA 96(12), 6745–6750 (1999), Data: http://microarray.princeton.edu/oncology/affydata/index.html
Yeung, K.Y., Bumgarner, R.E., Raftery, A.E.: Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21(10), 2394–2402 (2005)
Wit, E., McClure, J.: Statistics for Microarrays. John Wiley & Sons Ltd, Chichester (2004)
Giles, P.J., Kipling, D.: Normality of oligonucleotide microarray data and implications for parametric statistical analysis. Bioinformatics 19(17), 2254–2262 (2003)
Zhang, H., Yu, C.Y., Singer, B., Xiong, M.: Recursive partitioning for tumor classification with gene expression microarray data. Proc. National Academy of Sciences USA 98(12), 6730–6735 (2001)
Zhang, H., Yu, C.Y., Singer, B.: Cell and tumor classification using gene expression data: Construction of forests. Proc. National Academy of Sciences USA 100(7), 4168–4172 (2003)
Su, Y., Murali, T.M., Pavlovic, V., Schaffer, M., Kasif, S.: RankGene: identification of diagnostic genes based on expression data. Bioinformatics 19(12), 1578–1579 (2003), Software available from: http://genomics10.bu.edu/yangsu/rankgene/
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1999)
Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics 22(1), 79–86 (1951)
Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. Royal Society of London (A) 186, 453–461 (1946)
Golub, T.R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999), Data: http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi
Gordon, G.J., et al.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 62(17), 4963–4967 (2002), Data: http://www.chestsurg.org/publications/2002-microarray.aspx
Pomeroy, S.L., et al.: Prediction of central nervous system embryonal tumour outcome based on gene expresion. Nature 415(6870), 436–442 (2002), Data: http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi
Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. National Academy of Sciences USA 98(26), 15149–15154 (2001), Data: http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi
Shipp, M.A., et al.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 8(1), 68–74 (2002), Data: http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi
Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002), Data: http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wan, R., Takigawa, I., Mamitsuka, H. (2006). Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_5
Download citation
DOI: https://doi.org/10.1007/11960669_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68970-6
Online ISBN: 978-3-540-68971-3
eBook Packages: Computer ScienceComputer Science (R0)