Random Forest Algorithm, An Interactive Discussion

The random Forest algorithm has gained a significant interest in the recent past, due to its quality performance in several areas. A lot of new research work/survey reports related to different areas also reflects this. So, I decided to present a highly interactive tutorial on Random forest.

Random Forest in Research after-2012 to till date (a few references as per my interest)

1. Computer vision

On the study of Performance of Random Forest and SVM in Face Recognition, [1] reported that - the SVM achieved accuracy of 93.20%, but when optimized with different classifiers and kernel accuracy among all was 95.89%, 96.92%,
97.94%. Random Forest achieved accuracy of 97.17%. Similarly, [2] demonstrated that Random Forest regression can be used to generate
high quality response images.

2. Text Mining (including IR, NLP)

[3] describes a machine learning approach, a Random Forest (RF) classifier, to automatically compile bilingual dictionaries of technical terms from comparable corpora. [4] used random forest classifier to achieve 0.79 ROC-AUC at 0.76 precision and 0.76 recall in the detection of clickbait, i.e., short messages that lure readers to click a link.

3. Other Areas

according to the survey [5], With the data explosion in modern biology, and the rise in the data complexity in bioinformatics, as a non-parametric model, random forest provides a unique combination of prediction accuracy and model
interpretability. [6], noted the robustness of Random Forest-based gene selection methods.

Reference:

1. Kremic, E., & Subasi, A. (2015). Performance of Random Forest and SVM in Face Recognition. The International Arab Journal of Information Technology.
2. Cootes, Tim F., et al. "Robust and accurate shape model fitting using random forest regression voting." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 278-291.
3. Kontonatsios, G., Korkontzelos, I., Jun'ichi Tsujii, & Ananiadou, S. (2014, April). Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora. In EACL (pp. 111-116).
4. Potthast, M., K?psel, S., Stein, B., & Hagen, M. (2016, March). Clickbait Detection. In European Conference on Information Retrieval (pp. 810-817). Springer International Publishing.
5. Qi, Y. (2012). Random forest for bioinformatics. In Ensemble machine learning (pp. 307-323). Springer US.
6. Kursa, Miron Bartosz. "Robustness of Random Forest-based gene selection methods." BMC bioinformatics 15, no. 1 (2014): 1.