A Novel All Possible Sets Ensemble Linear Regression Scheme – A Case Study of Construction Accidents (Fatal Falls) Prediction

Full Article - PDF

Published: 2023-03-15

Page: 37-55


Rahul Konda *

Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India.

Ramesh Chandra Bagadi

Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India and Ramesh Bagadi Consulting LLC (R042752), Madison, 53715, Wisconsin, USA.

V. S. S. Kumar

Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India and NITTR Chennai, Ministry of HRD, Government of India, India.

Suresh Kumar N.

Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India.

*Author to whom correspondence should be addressed.


Abstract

In this research investigation, the authors have proposed an All Possible Sets Ensemble Linear Regression Scheme. In this case, all possible sets are considered and their predictors for a particularly considered future co-ordinate are evaluated. We again pro-rate these thusly found future values using an Inner Product value obtained by the normalized vector consisting of all the points of the given dataset under linear regression analysis (arranged in a chronological order) along with its future prediction value of the afore-considered future time co-ordinate of concern and any set normalized vector among the all possible sets of the data co-ordinates (also arranged in chronological order only that the missing points shall be denoted by zero) along with its future prediction value of the afore-considered future time co-ordinate of concern, in the computation of the Ensemble Weighted Average. The entire computational analysis is performed in an R Programming Software environment. For this analysis to be applicable with low computational complexity, the authors condense the data set to extract a Sub-Set from the given data set using a presented special data splitting novel criterion, for conducting the aforementioned analysis.

Keywords: Linear regression, R programming software


How to Cite

Konda, R., Bagadi, R. C., Kumar, V. S. S., & Kumar N., S. (2023). A Novel All Possible Sets Ensemble Linear Regression Scheme – A Case Study of Construction Accidents (Fatal Falls) Prediction. Asian Research Journal of Current Science, 5(1), 37–55. Retrieved from https://globalpresshub.com/index.php/ARJOCS/article/view/1781

Downloads

Download data is not yet available.

References

Breiman L. Using iterated bagging to debias regressions. Mach Learn. 2001;45(3):261-77-277. DOI: 10.1023/A:1017934522171

Rosen BE. Ensemble learning using decorrelated neural networks. Connect Sci. 1996;8(3-4):373-84,383. DOI: 10.1080/095400996116820

Perrone MP, Cooper LN. When networks disagree: ensemble methods for hybrid neural networks. In: Mammone RJ, editor. Neural networks for speech and image processing. Hall: Chapman. 1995;342-58. DOI: 10.1142/9789812795885_0025

Hashem S. Optimal linear combinations of neural networks [PhD thesis]. Purdue University; 1993.

Yankov D, DeCoste D, Keogh E. Ensembles of nearest neighbor forecasts. In: LNAI Eur Conference on Machine Learning. Springer; 2006;4212:545-56- 556.

Monirul Islam MM, Yao X, Murase K. A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Netw. 2003;14(4):820-34{834. DOI: 10.1109/TNN.2003.813832, PMID 18238062.

Tsang IW, Kocsor A, Kwok JT. Diversified svm ensembles for large data sets. In: LNAI Eur Conference on Machine Learning. Springer. 2006;4212:792-800-800.

Liu Y, Yao X. Ensemble learning via negative correlation. Neural Netw. 1999; 12(10):1399-404-1404. DOI: 10.1016/s0893-6080(99)00073-8, PMID 12662623.

Opitz DW, Shavlik JW. Generating accurate and diverse members of a neural-network ensemble. Adv Neural Inf Process Syst. 1996;8:535-541.

Lin H-T, Li L. Infinite ensemble learning with support vector machines. In: LNAI Eur Conference on Machine Learning. Springer; 2005;3720:242-54-254.

Pio A, Jorge M, Azevedo PJ. An experiment with association rules and classification: post-bagging and conviction. Singapore: Discovery sciences. Springer. 2005;LNCS 3735:137-149.

Azevedo PJ, Pio A, Jorge M. Iterative reordering of rules for building ensembles without relearning. In: LNCS 10th International Conference on Dicovery Science. Springer. 2007;4755:56-67-67.

Meyer D, Leisch F, Hornik K. The support vector machine under test. Neurocomputing. 2003;55(1-2):169-86{186. DOI: 10.1016/S0925-2312(03)00431-4

Bakker B, Heskes T. Clustering ensembles of neural network models. Neural Netw. 2003;16(2):261-9-269. DOI: 10.1016/S0893-6080(02)00187-9, PMID 12628611.

Tamon C, Xiang J. On the boosting pruning problem. In: LNCS Eur Conference on Machine Learning. Springer; 2000;1810:404-12-412.

Aksela M. Comparison of classifier selection methods for improving committee performance. In: LNCS International Workshop on Multiple Classifier Systems. Springer. 2003;2709:84-93-93.

Partridge D, Yates WB. Engineering multiversion neural-network systems. Neural Comput. 1996;8(4):869-93-893. DOI: 10.1162/neco.1996.8.4.869, PMID 8624963.

Zhou ZH, Tang W. Selective ensemble of decision trees. In: LNAI International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Springer; 2003;2639. 476-83-483.

All Possible Regressions, Available:https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/All_Possible_Regressions.pdf Available:https://www.ncss.com/software/ncss/regression-analysis-in-ncss/#Subset

All subsets regression. Available:https://webspace.maths.qmul.ac.uk/b.bogacka/SM_I_2013_LecturesWeek_10.pdf

Williams JD, Lindem AC, Williams JD, Lindem AC. Setwise regression analysis-A stepwise procedure for sets of variables. Educ Psychol Meas. 1971;31(3), Aut:747-48. DOI: 10.1177/001316447103100315 Available:https://www.thinkcalculator.com/texttool/combination-generator.php Available:https://en.wikipedia.org/wiki/Construction_site_safety. Wikipedia

Time Like And Hyper Time Like Systems”. R B Ideas Journal, Volume 1, Issue 4, Seventeenth Edition, September 2021. Independently Published by Amazon – Kindle Direct Publishing, USA. July 24th 2021, ISBN: 979-8542904870

Konda R, Bagadi RC, Kumar VSS, N, S. K. A novel all possible periodic sets ensemble linear regression scheme – a case study of construction accidents (fatal falls) prediction. Asian Research Journal of Current Science. 2023;5(1):28-36. Available:https://globalpresshub.com/index.php/ARJOCS/article/view/1776 Available:https://www.bls.gov/opub/ted/2022/a-look-at-falls-slips-and-trips-in-the-construction-industry.htm Available:https://matrixcalc.org/slu.html#solve-using-inverse-matrix-method%28%7B%7B2018,1,0,0,327%7D,%7B2015,1,0,0,344%2e5714286%7D%7D%29