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Advance Probabilistic Binary Decision Tree using SVM

by Anita Meshram, Roopam Gupta, Sanjeev Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 11
Year of Publication: 2014
Authors: Anita Meshram, Roopam Gupta, Sanjeev Sharma
10.5120/18956-0256

Anita Meshram, Roopam Gupta, Sanjeev Sharma . Advance Probabilistic Binary Decision Tree using SVM. International Journal of Computer Applications. 108, 11 ( December 2014), 26-30. DOI=10.5120/18956-0256

@article{ 10.5120/18956-0256,
author = { Anita Meshram, Roopam Gupta, Sanjeev Sharma },
title = { Advance Probabilistic Binary Decision Tree using SVM },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 11 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number11/18956-0256/ },
doi = { 10.5120/18956-0256 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:43.946903+05:30
%A Anita Meshram
%A Roopam Gupta
%A Sanjeev Sharma
%T Advance Probabilistic Binary Decision Tree using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 11
%P 26-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The probabilistic decision tree to an actual diagnosis database is in progress, where the performance of the probabilistic decision tree is tested in view of the size of the databases and the difficulties is that it implies for processing them. Here proposed an algorithm Advance Probabilistic Binary Decision Tree (APBDT) using SVM for solving large class problem and it performs better when increase the size of the database. APBDT-SVM combines Binary Decision Tree (BDT) and Probabilistic SVM is an effective way for solving multiclass problem. Probabilistic SVM uses standard SVM's output and sigmoid function to map the SVM output into probabilities. Using APBDT-SVM classification accuracy can be improved and training-testing time can be reduced.

References
  1. V. N. vapnik; the nature of statistical learning theory; Springer, Newyork, 1995.
  2. C. W. Hsu. , C. J. Lin; "A comparison methods for multiclass support vector machines"; IEEE transaction on neural networks, vol. 13, no. 2, pg. 415-425, March 2002.
  3. Y. Liu and Y. F. Zheng; "One-against-all multi-class SVM classification using reliability measures"; IEEE international joint conference on neural network (IJCNN); vol. 2, pg: 849-854, 31 July- 4 Aug. 2005.
  4. J. C. Platt, N. Cristianini and J. Shahere-Taylo;, "Large margin DAGs for multiclass classification"; Advances in neural information processing system, vol. 12, no. 3, pg. 547-553, 2000.
  5. G. madzarov, D. gjorgjevikj and I. chorbev; "A multi-class SVM classifier utilizing binary decision tree"; An international journal of computing and informatics, Informatica; vol. 33 number 2; ISSN0350-5596, Slovenia; pg:233-241, 2009.
  6. J. Platt; "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods"; in Advances in large margin classifiers, Cambridge, MIT press, 2000.
  7. G. sun, Z. Wang, M. Wang; "A new multi-classification method based on binary tree support vector machine"; 3rd international conference on innovative computing information and control (ICICIC) IEEE, Dalian, Liaoning; pg. 77; 18-20 June 2008.
  8. S. A. Mulay, P. R. Devale,G. V. Garje; "Intrusion detection system using support vector machine and decision tree"; International journal of computer application; vol. 3, Number 3, pg. 0975-8887; June 2010.
  9. M. Bala, R. K. Agrawal; "Optimal decision tree based multi-class support vector machine"; Informatica; school of computer science & system sciences, vol. 35, pg. 197-209; 2011.
  10. M. Arun Kumar, M. Gupta; "Fast multiclass SVM classification using decision tree based one-against-all method"; Springer, neural process lett, vol. 32, pg. 311-323; 25 Nov. 2010.
  11. A. Rocha, S. Goldenstein; "multiclass from binary: Expanding one-vs. -all, one vs. one and ECOC-based approaches"; IEEE transaction on neural networks and learning system; vol. X, number Y; Aug. 2013.
  12. B. Sidaoui, K. Sadouni; "Efficient approach one- versus- all binary tree for multiclass SVM"; Springer link; Transactions on engineering technologies; vol. 275, pg. 203-214; 2014.
  13. D. A. Cohen, E. A. Fernandez; SVMTOCP: "A Binary tree based SVM approach through optimal multiclass binarization"; Springer link; progress in pattern recognition, image analysis, computer vision and application; vol. 7441, pg. 472-478; 2012.
  14. G. madzarov, D. gjorgjevikj; "Evaluation of distance measures for multi-class classification in binary SVM decision tree"; Springer link; Artificial intelligence and soft computing lectures notes in computer science; vol. 6113, pg. 437-444; 2010.
  15. J. S. Uribe, N. Mechbal, M. Rebillat, K. Bouamama, M. Pengov; "Probabilistic Decision Tree using SVM for multiclass classification"; Conference on control and fault-tolerant system. IEEE; pg. 619 to 624; Oct 9-11, France 2013.
Index Terms

Computer Science
Information Sciences

Keywords

SVM Probabilistic SVM Binary decision tree separability measures

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