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Analyzing Random Forest Classifier with Different Split Measures

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Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

Abstract

Random forest is an ensemble supervised machine learning technique. The principle of ensemble suggests that to yield better accuracy, the base classifiers in the ensemble should be diverse and accurate. Random forest uses decision tree as base classifier. In this paper, we have done theoretical and empirical comparison of different split measures for induction of decision tree in Random forest and tested if there is any effect on the accuracy of Random forest.

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References

  1. Breiman, L.: Bagging predictors, Technical report No 421, September (1994).

    Google Scholar 

  2. Opitz, David: Maclin, richard: popular ensemble methods: an empirical study. J. Arti. Intel. 11, 169–198 (1999)

    MATH  Google Scholar 

  3. Brieman, Leo: Random forests. Machine Learning. 45, 5–32 (2001)

    Article  Google Scholar 

  4. Sikonja, M.R.: Improving random forests. In: Boulicaut, J.F., et al. (eds): Machine Learning, ECML 2004 Proceedings, LNCS, vol. 3201, PP. 359–370, Springer, Berlin (2004).

    Google Scholar 

  5. Rokach, Lior: Maimon, oded: top-down induction of decision trees classifiers-a survey. IEEE trans. syst. man. cyber. part c: appli. rev. 35(4), 476–487 (2005).

    Google Scholar 

  6. Badulescu, L.A.: The choice of the best attribute selection measure in DecisionTree induction, Annals of University of Craiova, Math. Comp. Sci. Ser. Vol. 34 (1) (2007).

    Google Scholar 

  7. Mingers, J.: An empirical comparison of selection measures for decision tree induction. Mach. Learn. 3, 319–342 (1989)

    Google Scholar 

  8. Robnik-Sikonja, M., Kononenko, I.: Attribute dependencies, understandability, and split selection in tree based models, Machine Learning: Proceedings of the 6th International Conference (ICML), 344–353 (1999).

    Google Scholar 

  9. Brieman, Leo: Technical note-some properties of splitting criteria. Mach. Learn. 24, 41–47 (1996)

    Google Scholar 

  10. Han, J., Kamber, M.: Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publisher, San Francisco (2006)

    Google Scholar 

  11. Buntine, Wray: Niblet, tim: a further comparison of splitting rules for decision tree induction. Mach. learn. 8, 75–85 (1992)

    Google Scholar 

  12. Kulkarni, V.Y., pradeep, K.S.: Random forest classifiers: a survey and future research directions. Int. J. Adv. Comput. ISSN 2051–0845. 36(1), 1144–1153 (2013).

    Google Scholar 

  13. Liu, W.Z., White, A.P.: The importance of attribute selection measures in decision tree induction. Mach. Learn. 15, 25–41 (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. COEP, Pune, India

    Vrushali Y. Kulkarni

  2. MIT, Pune, India

    Vrushali Y. Kulkarni

  3. MIT, Pune, India

    Manisha Petare

  4. HPC and R&D, CDAC, Pune, India

    P. K. Sinha

Authors
  1. Vrushali Y. Kulkarni
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  2. Manisha Petare
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  3. P. K. Sinha
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Corresponding author

Correspondence to Vrushali Y. Kulkarni .

Editor information

Editors and Affiliations

  1. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    B. V. Babu

  2. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  5. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  6. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Kanad Ray

  7. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Umesh Gupta

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Kulkarni, V.Y., Petare, M., Sinha, P.K. (2014). Analyzing Random Forest Classifier with Different Split Measures. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_74

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_74

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

  • eBook Packages: EngineeringEngineering (R0)

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