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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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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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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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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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

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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

[1] P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Boston: Pearson Addison Wesley, 2005.

[2] Y. Freund and R. E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
[CrossRef] [Web of Science Times Cited 4423] [SCOPUS Times Cited 5878]

[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]

[6] J. R. Quinlan, "Induction of decision trees," Mach Learn, vol. 1, no. 1, pp. 81-106, Mar. 1986.
[CrossRef] [SCOPUS Times Cited 7761]

[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.

[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 101]

[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 25]

References Weight

Web of Science® Citations for all references: 4,541 TCR
SCOPUS® Citations for all references: 13,982 TCR

Web of Science® Average Citations per reference: 197 ACR
SCOPUS® Average Citations per reference: 608 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 2017-11-21 20:30 in 65 seconds.

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Faculty of Electrical Engineering and Computer Science
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