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Comparative Analysis of Serial Decision Tree Classification Algorithms
Matthew Nwokejizie Anyanwu, Sajjan Shiva
Pages - 230 - 240     |    Revised - 05-08-2009     |    Published - 01-09-2009
Volume - 3   Issue - 3    |    Publication Date - June 2009  Table of Contents
Decision tree, Classification Algorithm, data
Classification of data objects based on a predefined knowledge of the objects is a data mining and knowledge management technique used in grouping similar data objects together. It can be defined as supervised learning algorithms as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. Classification algorithms have a wide range of applications like churn prediction, fraud detection, artificial intelligence, and credit card rating etc. Also there are many classification algorithms available in literature but decision trees is the most commonly used because of its ease of implementation and easier to understand compared to other classification algorithms. Decision Tree classification algorithm can be implemented in a serial or parallel fashion based on the volume of data, memory space available on the computer resource and scalability of the algorithm. In this paper we will review the serial implementations of the decision tree algorithms, identify those that are commonly used. We will also use experimental analysis based on sample data records (Statlog data sets) to evaluate the performance of the commonly used serial decision tree algorithms
CITED BY (9)  
1 M. Andrade and M. A. M. Ferreira, “Criminal and Civil Identification with DNA Databases Using Bayesian Networks”, International Journal of Security (IJS), 3(4), pp. 65 – 74, 2009.
2 M. K. Shan, “Discovering Color Styles from Fine Art Images of Impressionism”, International Journal of Computer Science and Security (IJCSS), 3(4), pp. 314 – 324, 2009.
3 D. Lavanya and Dr. K.U. Rani, “Performance Evaluation of Decision Tree Classifiers on Medical Datasets”, International Journal of Computer Applications, 26(4), pp. 1-4, 2011.
4 S. Soni and S. Shrivastava, “Classification of Indian Stock Market Data Using Machine Learning Algorithms”, International Journal on Computer Science and Engineering, 02(09), pp. 2942-2946, 2010.
5 T.N. Tsai, “Development of a Soldering Quality Classifier System Using a Hybrid Data Mining Approach”, Expert Systems with Applications, 39(5), pp. 5727–5738, December 2011.
6 W. Okori and J. Obua, “Machine Learning Classification Technique for Famine Prediction”, in Proceedings of the World Congress on Engineering 2011 Vol. II WCE 2011, London, U.K., July 6 - 8, 2011.
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8 W. Okori and J. Obua, “Supervised Learning Algorithms for Famine Prediction”, Applied Artificial Intelligence: An International Journal, 25(9), pp. 822-835, 2011.
9 Y. M. Lazim and F. Mohamed, “Applying Rough Set Theory in Multimedia Data Classification”, International Journal on New Computer Architectures and Their Applications, 1(3), pp. 706 - 716, 2011.
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1 Anyanwu, M., and Shiva, S. (2009). Application of Enhanced Decision Tree Algorithm to Churn Analysis. 2009 International Conference on Artificial Intelligence and Pattern Recognition (AIPR-09), Orlando Florida
2 BA?DULESCU, L. A. (n.d). Data mining algorithms based on decision trees. Annals of the ORADEABA?DULESCU, L. A. (n.d). Data mining algorithms based on decision trees. Annals of the ORADEA university. From http://www.imtuoradea.ro/auo.fmte/files2006/MIE files/Laviniu%20Aurelian%20Badulescu%201.pdf Retrieved date: May 13, 2009
3 Breiman, L., Friedman, J., Olshen, L and Stone, J. (1984). Classification and Regression trees. Wadsworth Statistics/Probability series. CRC press Boca Raton, Florida, USA.
4 Du, W., Zhan, Z. (2002). Building decision tree classifier on private data, Proceedings of the IEEE international conference on Privacy, security and data mining, pp.1-8, Maebashi City, Japan
5 Garofalakis, M., Hyun, D., Rastogi, R. and Shim, K. (200). Efficient algorithms for constructing decision trees with constraints. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 335 - 339
6 Gehrke, J., Ramakrishnan, R., Ganti, V. (1998). RainForest - a Framework for Fast Decision Tree Construction of Large Datasets.Proceedings of the 24th VLDB conference, New York, USA. pp.416- 427
7 Gehrke, J., Ramakrishnan, R., Ganti, V. (1998). RainForest - a Framework for Fast Decision Tree Construction of Large Datasets, Proceedings of the 24th VLDB conference, New York, USA. pp.416- 427
8 Hunt, E.B., Marin. and Stone,P.J. (1966). Experiments in induction, Academic Press, New York.
9 Khoshgoftaar, T.M and Allen, E.B. (1999). Logistic regression modeling of software quality. International Journal of Reliability, Quality and Safety Engineering, vol. 6(4, pp. 303-317.
10 Kufrin, R. (1997). Decision trees on parallel processors. In J. Geller, H. Kitano, and C. B. Suttner, editors, parallel Processing for Artificial Intelligence 3 Elsevier Science
11 Lewis, R.J. (200). An Introduction to Classification and Regression Tree (CART) Analysis. 2000 Annual Meeting of the Society for Academic Emergency Medicine, Francisco, California
12 Ling, X. C., Yang, Q., Wang, J and Zhang, S. (2004). Decision trees with minimal costs. Proceedings of the 21st International Conference on Machine Learning, Banff, Canada
13 Lippmann, R. (1987). An Introduction to computing with neural nets. IEEE ASSP Magazine, vol. (22)
14 Mehta, M., Agrawal, R., and Rissanen, J. (1995). MDL-based decision tree pruning. International conference on knowledge discovery in databases and data mining (KDD-95) Montreal, Canada
15 Mehta, M., Agrawal, R., and Rissanen, J. (1996). SLIQ: A fast scalable classifier for data mining. In EDBT 96, Avignon, France
16 Michie, D., Spiegelhalter, D., J., and Taylor, C., C. (1994). Machine Learning, Neural and Statistical Class fication, Ellis Horword
17 Peng, W., Chen, J. and Zhou,H. An Implementation of IDE3 Decision Tree Learning Algorithm. From web.arch.usyd.edu.au/ wpeng/DecisionTree2.pdf Retrieved date: May 13, 2009
18 Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I. (2002). Decision trees: an overview and their use in medicine, Journal of Medical Systems Kluwer Academic/Plenum Press, vol.26, Num. 5, pp.445-463.
19 Pješivac-Grbovic, J., Angskun, T., Bosilca, G., Fagg, G.E., Dongarra, J. J. (2006). Decision trees and MPI collective algorithm selection problem. Tech. Rep. UT-CS-06-586, The University of Tennessee at Knoxville, Computer Science Department. From < http : //www.cs.utk.edu/library/2006.html >
20 Peng, W., Chen, J., and Zhou., H (n.d). An Implementation of ID3?? decision tree learning algorithm. University of New South Wales, School of Computer Science and Engineering, Sydney, NSW 2032, Australia
21 Quinlan, J. R. (1986). Induction of decision trees. Machine Leaning, vol (1), pp.81-106
22 Quinlan, J. R. (1987). Simplifying decision trees, International Journal of Machine Studies, number27, pp. 221-234.
23 Quinlan, J. R. (1993). C45: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
24 Utgoff, P and Brodley, C. (1990). An Incremental Method for Finding Multivariate Splits for Decision Trees, Machine Learning: Proceedings of the Seventh International Conference pp.58.
25 Rastogi, R., and Shim, K. (1998). PUBLIC: A decision tree classifier that integrates building and pruning. Proceedings of the 24th VLDB Conference. New York. pp. 404-415
26 Sattler, K and Dunemann, O. (2001).SQL database primitives for decision tree classifiers Proceedings of the tenth international conference on Information and knowledge management, pp.379-386
27 Shafer, J., Agrawal, R., and Mehta, M. (1996). Sprint: A scalable parallel classifier for data mining. Proceedings of the 22nd international conference on very large data base. Mumbai (Bombay), India
28 Srivastava, A., Singh, V., Han, E., and Kumar, V .(1997). An efficient, scalable, parallel classifier for data mining . University of Minnesota, Computer Science, technical report, USA.
29 Srivastava, A., Singh, V. (n.d). Methods to reduce I/O for decision tree classifiers. IBM T.J Watson Research center.
30 Srivastava, A., Singh,V., Han,E., and Kumar, V .(1998). Parallel Formulations of Decision- Tree Classification Algorithms. Data Mining and Knowledge Discovery, an international journal, pp.237-261
31 Tan, P., Steinbach, M. and Kumar, V. (2006). Introduction to Data
32 Wen, X., Hu, G., Yang, X. (2008). CBERS-02 Remote Sensing Data Mining Using Decision Tree Algorithm, First International Workshop on Knowledge Discovery and Data Mining, pp.86-89
33 Xu, M., Wang, J. and Chen, T. (2006). Improved decision tree algorithm: ID3+, Intelligent Computing in Signal Processing and Pattern Recognition, Vol.345, pp.141-149
Mr. Matthew Nwokejizie Anyanwu
- United States of America
Dr. Sajjan Shiva
University of Memphis - United States of America