Random Forest for the Real Forests

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

A forest is a vast area of land covered predominantly with trees and undergrowth. In this paper, adhering to cartographic variables, we try to predict the predominant kind of tree cover of a forest using the Random Forests (RF) classification method. The study classifies the data into seven classes of forests found in the Roosevelt National Forest of Northern Colorado. With sufficient data to create a classification model, the RF classifier gives reasonably accurate results. Fine-tuning of the algorithm parameters was done to get promising results. Besides that a dimensionality check on the dataset was conducted to observe the possibilities of dimensionality reduction.

Keywords

Random forests Dimensionality reduction Forests’ classification 

Notes

Acknowledgments

The authors would like to thank Kaggle2 for hosting the above problem. This dataset was provided by Jock A. Blackard and Colorado State University. We also thank the UCI machine learning repository for hosting3 the dataset [16].

References

  1. 1.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2009)Google Scholar
  2. 2.
    Hughes, G.: On the mean accuracy of statistical pattern recognizers information theory. IEEE Trans. 14, 55–63 (1968)Google Scholar
  3. 3.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9, 1545–1588 (1997)CrossRefGoogle Scholar
  4. 4.
    Leo, B.: Random Forests Machine Learning 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Zhang, J., Zulkernine, M.: Network intrusion detection using random forests. In: Third Annual Conference on Privacy, Security and Trust (PST), pp. 53–61 (2005)Google Scholar
  6. 6.
    Altendrof, J.D.E., Brende, P., Lessard, L.: Fraud detection for online retail using random forests Technical Report (2005)Google Scholar
  7. 7.
    Dittman, D., Khoshgoftaar, T., Wald,R., Napolitano, A.: Random forest: a reliable tool for patient response prediction. Bioinformatics and Biomedicine Workshops IEEE International Conference (2011)Google Scholar
  8. 8.
    Srivastava, A., Chakrabarti, S., Das, S., Ghosh, S., K. Jayaraman, V.: Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) Advances in Intelligent Systems and Computing, vol. 201, pp. 485–494 (2013)Google Scholar
  9. 9.
    Boinee, P., Angelis, A. D., Foresti, G.L.: Ensembling classifiers—an application to image data classification from cherenkov telescope experiment IEC (Prague), pp. 394–398 (2005)Google Scholar
  10. 10.
    Geng, W., Cosman, P., Berry, C., Feng, Z., Schafer, W.: Automatic tracking, feature extraction and classification of C. elegans phenotypes. IEEE Trans. Biomed. Eng. (2004)Google Scholar
  11. 11.
    Diaz-Uriarte, R., Alvarez de Andres, S: Gene selection and classification of microarray data using random forest BMC Bioinformatics, vol. 7, pp. 1–13 (2006)Google Scholar
  12. 12.
    Maragoudakis, M., Loukis, E., Pantelides, P.: Random forests identification of gas turbine faults. In: 19th International Conference on Systems Engineering (2008)Google Scholar
  13. 13.
    Hu, H., Zahorian, S.: Dimensionality reduction methods for HMM phonetic recognition. In: Acoustics Speech and Signal Processing, IEEE International Conference (2010)Google Scholar
  14. 14.
    Bostrom, H.: Estimating Class Probabilities in Random Forests. In: Sixth International Conference on Machine Learning and Applications (2007)Google Scholar
  15. 15.
    Khoshgoftaar, T., Golawala, M., Hulse, J.: An empirical study of learning from imbalanced data using random forest. In: 19th IEEE International Conference on Tools with Artificial Intelligence (2007)Google Scholar
  16. 16.
    Lichman, M.: UCI MAchine Learning Repository, Irvine. University of California, School of Information and Computer Science, CA (2003)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia

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