Skip to main content
SpringerLink
Log in

Learning random forests for ranking

  • Research Article
  • Published:
Frontiers of Computer Science in China Aims and scope Submit manuscript

Abstract

The random forests (RF) algorithm, which combines the predictions from an ensemble of random trees, has achieved significant improvements in terms of classification accuracy. In many real-world applications, however, ranking is often required in order to make optimal decisions. Thus, we focus our attention on the ranking performance of RF in this paper. Our experimental results based on the entire 36 UC Irvine Machine Learning Repository (UCI) data sets published on the main website of Weka platform show that RF doesn’t perform well in ranking, and is even about the same as a single C4.4 tree. This fact raises the question of whether several improvements to RF can scale up its ranking performance. To answer this question, we single out an improved random forests (IRF) algorithm. Instead of the information gain measure and the maximum-likelihood estimate, the average gain measure and the similarity-weighted estimate are used in IRF. Our experiments show that IRF significantly outperforms all the other algorithms used to compare in terms of ranking while maintains the high classification accuracy characterizing RF.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Provost F, Domingos P. Tree induction for probability-based ranking. Machine Learning, 2003, 52(3): 199–215

    Article  MATH  Google Scholar 

  2. Ling C X, Yan R J. Decision tree with better ranking. In: Proceedings of 20th International Conference on Machine Learning. 2003, 480–487

  3. Jiang L X, Li C Q, Cai Z H. Learning decision tree for ranking. Knowledge and Information Systems, 2009, 20(1): 123–135

    Article  Google Scholar 

  4. Jiang L X, Wang D H, Zhang H, Cai Z H, Huang B. Using instance cloning to improve naive Bayes for ranking. International Journal of Pattern Recognition and Artificial Intelligence, 2008, 22(6): 1121–1140

    Article  Google Scholar 

  5. Bradley A P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997, 30(7): 1145–1159

    Article  Google Scholar 

  6. Hand D J, Till R J. A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning, 2001, 45(2): 171–186

    Article  MATH  Google Scholar 

  7. Ling C X, Huang J, Zhang H. Auc: a statistically consistent and more discriminating measure than accuracy. In: Proceedings of 18th International Joint Conference on Artificial Intelligence. 2003, 519–526

  8. Quinlan J R. C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann, 1992

    Google Scholar 

  9. Mitchell T M. Machine Learning. New York: McGraw-Hill, 1997

    MATH  Google Scholar 

  10. Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

    Article  MATH  Google Scholar 

  11. Dietterich T G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 2000, 40(2): 139–157

    Article  Google Scholar 

  12. Breiman L. Bagging Predictors. Machine Learning, 1996, 24(2): 123–140

    MathSciNet  MATH  Google Scholar 

  13. Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine Learning, 1999, 36(1–2): 105–139

    Article  Google Scholar 

  14. Witten I H, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. San Francisco: Morgan Kaufmann, 2005

    MATH  Google Scholar 

  15. Quinlan J R. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106

    Google Scholar 

  16. Wang D H, Jiang L X. An improved attribute selection measure for decision tree induction. In: Proceedings of 4th International Conference on Fuzzy Systems and Knowledge Discovery. 2007, 654–658

  17. De Mántaras R L. A distance-based attribute selection measure for decision tree induction. Machine Learning, 1991, 6(1): 81–92

    Article  Google Scholar 

  18. Pazzani M J, Merz C J, Murphy P M, Ali K. Hume T, Brunk C. Reducing misclassification costs. In: Proceedings of 11th International Conference on Machine Learning. 1994, 217–225

  19. Bradford J P, Kunz C, Kohavi R, Brunk C, Brodley C E. Pruning decision trees with misclassification costs. In: Proceedings of 10th European Conference on Machine Learning. 1998, 131–136

  20. Provost F J, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. In: Proceedings of 15th International Conference on Machine Learning. 1998, 445–453

  21. Jiang L X, Wang D H, Cai Z H. Scaling up the accuracy of bayesian network classifiers by m-estimate. In: Proceedings of 3rd International Conference on Intelligent Computing. 2007, 475–484

  22. Smyth P, Gray A, Fayyad UM. Retrofitting decision tree classifiers using kernel density estimation. In: Proceedings of 12th International Conference on Machine Learning. 1995, 506–514

  23. Nadeau C, Bengio Y. Inference for the generalization error. Machine Learning, 2003, 52(3): 239–281

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Liangxiao Jiang

Authors
  1. Liangxiao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liangxiao Jiang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, L. Learning random forests for ranking. Front. Comput. Sci. China 5, 79–86 (2011). https://doi.org/10.1007/s11704-010-0388-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-010-0388-5

Keywords

Search

Navigation