Pratheek B R
ABSTRACT
This paper introduces rFITb, a distributed computing platform that enables the execution of computationally intensive random forest jobs on personal devices such as smartphones and personal computers. The platform leverages the increased computational capacity of personal devices to distribute and execute jobs globally, providing an efficient alternative to cloud-based services. The paper describes rFITb's architecture and design optimizations, along with a comparative evaluation of its performance against Python's sklearn ensemble random forest classifier on various datasets. The results show that rFITb outperforms the sklearn classifier in terms of model time, while also providing a mechanism for managing failure-prone volunteers.
- Anderson, David P. "Boinc: A system for public-resource computing and storage." In Fifth IEEE/ACM international workshop on grid computing, pp. 4-10. IEEE, 2004.Google Scholar
- Tahani Daghistani, "Comparison of Statistical Logistic Regression and RandomForest Machine Learning Techniques in Predicting Diabetes." Journal of Advances in Information Technology, Vol. 11, No. 2, pp. 78-83, May 2020.Google ScholarCross Ref
- Changro Lee, "Random Forest with Transfer Learning: An Application to Vehicle Valuation." Journal of Advances in Information Technology, Vol. 13, No. 4, pp. 326-331, August 2022.Google ScholarCross Ref
- Cushing, Reginald, GaneshwaraHerawanHananda Putra, Spiros Koulouzis, Adam Belloum, Marian Bubak, and Cees De Laat. "Distributed computing on an ensemble of browsers." IEEE Internet Computing 17, no. 5 (2013): 54-61.Google ScholarDigital Library
- Meeds, Edward, Remco Hendriks, Said Al Faraby, Magiel Bruntink, and Max Welling. "MLitB: machine learning in the browser." PeerJ Computer Science 1 (2015): e11.Google ScholarCross Ref
- Pan, Yao, Jules White, Yu Sun, and Jeff Gray. "Gray computing: An analysis of computing with background javascript tasks." In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1, pp. 167-177. IEEE, 2015.Google Scholar
- Pan Yao, Jules White, Yu Sun, and Jeff Gray. "Gray computing: A framework for computing with background javascript tasks." IEEE Transactions on Software Engineering 45, no. 2 (2017): 171-193.Google Scholar
- Ujhelyi, Matúš, Peter Lacko, and Aurel Paulovič. "Task scheduling in distributed volunteer computing systems." In 2014 IEEE 12th international symposium on intelligent systems and informatics (SISY), pp. 111-114. IEEE, 2014.Google Scholar
- Morell, José Á., Andrés Camero, and Enrique Alba. "Jsdoop and tensorflow. js: Volunteer distributed web browser-based neural network training." IEEE Access 7 (2019): 158671-158684.Google ScholarCross Ref
- Matsuo, Hiroyuki, Shinsuke Matsumoto, Yoshiki Higo, and Shinji Kusumoto. "Madoop: Improving Browser-Based Volunteer Computing Based on Modern Web Technologies." In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 634-638. IEEE, 2019.Google Scholar
- Chen, Huey-Ling, and Chung-Ta King. "Eager scheduling with lazy retry in multiprocessors." Future Generation Computer Systems 17, no. 3 (2000): 215-226.Google ScholarDigital Library
- Biswas, Tarun, PratyayKuila, and Anjan Kumar Ray. "Multi-level queue for task scheduling in heterogeneous distributed computing system." In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1-6. IEEE, 2017.Google Scholar
- R.A. Fisher. (1988). Iris Data Set. Retrieved 25-12-2022 from https://archive.ics.uci.edu/ml/datasets/irisGoogle Scholar
- Ronny Kohavi and Barry Becker. (1996). Adult Data Set. Retrieved 25-12-2022 from https://archive.ics.uci.edu/ml/datasets/adult.Google Scholar
- Micheal OW. (2020). Cleaned Day-Trading Training Data, Version2. Retrieved 25-12-2022 from https://www.kaggle.com/datasets/dawerty/cleaned-daytrading-training-data.Google Scholar
- Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi. (2015). Credit card fraud detection. Retrieved 25-12-2022 from https://datahub.io/machine-learning/creditcard.Google Scholar
- Scikit-learn: Machine Learning in Python, Pedregosa , JMLR 12, pp. 2825-2830, 2011.Google Scholar
Index Terms
- rFITb: Random Forest in the Browser
Recommendations
Combining bagging, boosting, rotation forest and random subspace methods
Bagging, boosting, rotation forest and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-classifiers. Boosting and rotation forest ...
Reinforced random forest
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image ProcessingReinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to ...
A comparison of random forest based algorithms: random credal random forest versus oblique random forest
AbstractRandom forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate ...
Comments