LASH Tree: LASSO Regression Hoeffding for Streaming Data
DOI:
https://doi.org/10.61841/8mzqdz79Keywords:
Hoeffding Tree,, LASSO Regression, Prediction accuracy, Model adaptability.Abstract
Streaming data is a challenging research area for the last two decades which comes in high volume and rapid speed and cannot be stored using existing memory. Dealing with model adaptability with evolving data over time and memory usage arethe major challenges in streaming data predictive models. Recently there is a rising attention in developing Regression Tree models due to it’s high interpretability and accuracy. Additionally, the linear function at the leaf node evaluates the target variable more accurately by analysing the correlation between predictor variables and target variable. The proposed LASSO Regression Hoeffding Tree (LASH Tree) is a Regression Tree model which incorporates LASSO Regression with Hoeffding Tree that produces better predictions and better insights. In this paper, an exhaustive empirical testing of the proposed methodology is performed and compared with other standard model like CART, Hoeffding based Linear Regression Model (ORTO) using solar energy data set. The obtained results show that the proposed LASH Tree significantly outperforms the existing approaches and it is proved that there is boosting of accuracy and usedless memory usage when compared with other algorithms.
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References
1. Joao Gama , Raquel Sebastiao,” On evaluating Stream Learning Algorithms” in Mach Learn 90: 317 -346 Springer Publication [2013]
2. Lizhe Zun , Yangzi Guo, Adrian Barbo,” A Novel Framework for Online Supervised Learning with Feature Selection” in rXiv:1803.11521v4 [stat.ML] Dec [2018]
3. Robert Tibshirani ,”Regression Shrinkage and selection via lasso” January Journal of Royal Statistics Society, B Series, [1995]
4. Pedro Domingos, Geoff Hulten, “ Mining High-Speed Data Streams”, in KDD 2000, Boston, MA USA © ACM 1-58113-233-6/00/08 [2000]
5. D.Christy Sujatha , Dr,J.Gna Jayanthi, “Meta_LASH Tree : Bagging at Meta Level Using LASSO Regression Hoeffding Tree for Streaming Data” in the third International Conference on Trends in Electronics and Informatics (ICOEI 2019) IEEE Xplore Part Number: CFP19J32-ART; ISBN: 978-1-5386- 9439-8 ,2019 IEEE.
6. Elena Ikonomovska , Jo˜ao Gama , Bernard ˇZenko “Speeding Up Hoeffding-Based Regression Trees with Options” in Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA,[2011]
7. L. Breiman, J.Friedman, R.Olshen,C.Stone, “Classification and Regression Trees” in Journal of Engineering, Chapman and Hall, New York,[1993]
8. Iman Kamkar ,”Stable feature selection for clinical prediction: exploiting ICD Tree stricture using Tree Lasso “, in Journal of Bio medical Informatics 53, 277-290[2015]
9. Ricardo Pio Monti, Christoforos Anagnostopoulos, and Giovanni Montana “Adaptive regularization for Lasso models in the context of non-stationary data streams” in arXiv:1610.09127v2 [stat.ML] 14 December 2017
10. Feihan Lua and Eva Petkova “A comparative study of variable selection methods in the context of developing psychiatric screening instruments” in Statistics in Medicine , Research Article [2013]
11. Kai Chen ,Yang Chin “An Ensemble Learning Algorithm Based on Lasso Selection” in IEEE conference [2010].
12. Sanjiban Sekar Roy ,Avik Basu “Stock Market Forecasting using LASSO Linear regression model”in Afro-European Conf. for Ind. Advancement, an Advances in Intelligent Systems and Computing , DOI: 10.1007/978-3-319-13572-4_31.Springer International Publication Switzerland [2015]
13. http://dkasolarcentre.com.auhttp://dkasolarcentre.com.au
14. Hewageegana h. G. S. P, arawwawala l. D. A. M. , ariyawansa h. A. S, tissera m. H. A, dammaratana i. (2016) a review of skin diseases depicted in sanskrit original texts with special reference to ksudra kushtha. Journal of Critical Reviews, 3 (3), 68-73.
15. Dr.Sundararaju,K., & Rajesh,T. (2016). Control Analysis of Statcom under Power System Faults.
International Journal of Communication and Computer Technologies, 4(1), 46-50.
16. Ban Maheskumar N., &Prof.Sayed Akhtar, H. (2016). An online and offline Character Recognition Using Image Processing Methods-A Survey. International Journal of Communication and Computer Technologies, 4(2), 102-107.
17. Peter, S. An analytical study on early diagnosis and classification of Diabetes Mellitus (2014) Bonfring International Journal on Data Mining, 4 (2), pp. 7-11.
18. Murthy, N.H.S., Meenakshi, M. ANN model to predict coronary heart disease based on risk factors (2013) Bonfring Int. J. Man Mach Interface, 3 (2), pp. 13-18.
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