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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  1/2014 - 10

Fast Decision Tree Algorithm

PURDILA, V. See more information about PURDILA, V. on SCOPUS See more information about PURDILA, V. on IEEExplore See more information about PURDILA, V. on Web of Science, PENTIUC, S.-G. See more information about PENTIUC, S.-G. on SCOPUS See more information about PENTIUC, S.-G. on SCOPUS See more information about PENTIUC, S.-G. on Web of Science
 
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Download PDF pdficon (607 KB) | Citation | Downloads: 530 | Views: 1,965

Author keywords
algorithm, chi-merge, classification, data compression, decision tree, pruning

References keywords
decision(10), tree(7), data(7), pruning(6), mining(6), trees(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 65 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01010
Web of Science Accession Number: 000332062300010
SCOPUS ID: 84894631111

Abstract
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There is a growing interest nowadays to process large amounts of data using the well-known decision-tree learning algorithms. Building a decision tree as fast as possible against a large dataset without substantial decrease in accuracy and using as little memory as possible is essential. In this paper we present an improved C4.5 algorithm that uses a compression mechanism to store the training and test data in memory. We also present a very fast tree pruning algorithm. Our experiments show that presented algorithms perform better than C5.0 in terms of speed and classification accuracy in most cases at the expense of tree size - the resulting trees are larger than the ones produced by C5.0. The data compression and pruning algorithms can be easily parallelized in order to achieve further speedup.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 4509] [SCOPUS Times Cited 5973]


[3] S. Chakrabarti, Data mining: know it all. Burlington, MA: Elsevier/Morgan Kaufmann Publishers, 2009.

[4] E. C. Vasconcellos, R. R. de Carvalho, R. R. Gal, F. L. LaBarbera, H. V. Capelato, H. F. C. Velho, M. Trevisan, and R. S. R. Ruiz, "Decision Tree Classifiers for Star/Galaxy Separation," The Astronomical Journal, vol. 141, no. 6, p. 189, Jun. 2011.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 19]


[5] K.-H. Kim and J.-H. Kim, "Domain Independent Vocabulary Generation and Its Use in Category-based Small Footprint Language Model," Advances in Electrical and Computer Engineering, vol. 11, no. 1, pp. 77-84, 2011.
[CrossRef] [Full Text] [Web of Science Record] [SCOPUS Times Cited 1]


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[CrossRef] [SCOPUS Times Cited 7909]


[7] S. L. Salzberg, "C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993," Mach Learn, vol. 16, no. 3, pp. 235-240, Sep. 1994.
[CrossRef]


[8] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, "Classification and Regression Trees (POD)," 1999.

[9] M. Mehta, R. Agrawal, and J. Rissanen, "SLIQ: A Fast Scalable Classifier for Data Mining," in Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, London, UK, UK, 1996, pp. 18-32.

[10] J. C. Shafer, R. Agrawal, and M. Mehta, "SPRINT: A Scalable Parallel Classifier for Data Mining," in Proceedings of the 22th International Conference on Very Large Data Bases, San Francisco, CA, USA, 1996, pp. 544-555.

[11] W.-Y. Loh and Y.-S. Shih, Split Selection Methods for Classification Trees. 1997.

[12] F. Provost and V. Kolluri, "A Survey of Methods for Scaling Up Inductive Algorithms," Data Mining and Knowledge Discovery, vol. 3, no. 2, pp. 131-169, Jun. 1999.
[CrossRef] [Web of Science Times Cited 103]


[13] P. Huber, "From Large to Huge: A Statistician's Reactions to KDD & DM," 1997, p. 304.

[14] R. Kerber, "ChiMerge: discretization of numeric attributes," in Proceedings of the tenth national conference on Artificial intelligence, San Jose, California, 1992, pp. 123-128.

[15] J. Ouyang, N. Patel, and I. K. Sethi, "Chi-Square Test Based Decision Trees Induction in Distributed Environment," in IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08, 2008, pp. 477-485.

[16] J. R. Quinlan and R. L. Rivest, "Inferring decision trees using the minimum description length principle," Inf. Comput., vol. 80, no. 3, pp. 227-248, Mar. 1989.
[CrossRef] [SCOPUS Times Cited 296]


[17] D. Jensen and M. D. Schmill, "Adjusting for Multiple Comparisons in Decision Tree Pruning," in KDD, 1997, pp. 195-198.

[18] M. Kearns and Y. Mansour, "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization," in In Proceedings of the 15th International Conference on Machine Learning, 1998, pp. 269-277.

[19] W. Zhang and Y. Li, "A Post-Pruning Decision Tree Algorithm Based on Bayesian," in 2013 Fifth International Conference on Computational and Information Sciences (ICCIS), 2013, pp. 988-991.
[CrossRef] [SCOPUS Times Cited 2]


[20] H. Guo, M. Fan, and Y. Ye, "Forest pruning based on Tree-Node Order," in 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2011, vol. 3, pp. 71-76.

[21] J. Chen, X. Wang, and J. Zhai, "Pruning Decision Tree Using Genetic Algorithms," in International Conference on Artificial Intelligence and Computational Intelligence, 2009. AICI '09, 2009, vol. 3, pp. 244-248.

[22] W. N. H. W. Mohamed, M. N. M. Salleh, and A. H. Omar, "A comparative study of Reduced Error Pruning method in decision tree algorithms," in 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2012, pp. 392-397.
[CrossRef] [SCOPUS Times Cited 28]




References Weight

Web of Science® Citations for all references: 4,629 TCR
SCOPUS® Citations for all references: 14,228 TCR

Web of Science® Average Citations per reference: 201 ACR
SCOPUS® Average Citations per reference: 619 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-02-15 17:24 in 76 seconds.




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