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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2009)
Hughes, G.: On the mean accuracy of statistical pattern recognizers information theory. IEEE Trans. 14, 55–63 (1968)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9, 1545–1588 (1997)
Leo, B.: Random Forests Machine Learning 45(1), 5–32 (2001)
Zhang, J., Zulkernine, M.: Network intrusion detection using random forests. In: Third Annual Conference on Privacy, Security and Trust (PST), pp. 53–61 (2005)
Altendrof, J.D.E., Brende, P., Lessard, L.: Fraud detection for online retail using random forests Technical Report (2005)
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)
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)
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)
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)
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)
Maragoudakis, M., Loukis, E., Pantelides, P.: Random forests identification of gas turbine faults. In: 19th International Conference on Systems Engineering (2008)
Hu, H., Zahorian, S.: Dimensionality reduction methods for HMM phonetic recognition. In: Acoustics Speech and Signal Processing, IEEE International Conference (2010)
Bostrom, H.: Estimating Class Probabilities in Random Forests. In: Sixth International Conference on Machine Learning and Applications (2007)
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)
Lichman, M.: UCI MAchine Learning Repository, Irvine. University of California, School of Information and Computer Science, CA (2003)
Acknowledgments
The authors would like to thank KaggleFootnote 2 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 hostingFootnote 3 the dataset [16].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Agrawal, S., Rana, S., Ahmad, T. (2016). Random Forest for the Real Forests. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_32
Download citation
DOI: https://doi.org/10.1007/978-81-322-2526-3_32
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2525-6
Online ISBN: 978-81-322-2526-3
eBook Packages: EngineeringEngineering (R0)