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What is Multivariate Adaptive Regression Spline

Handbook of Research on Waste Management Techniques for Sustainability
MARS is a nonparametric regression modeling technique based on split and conquer approach in which the training dataset are segregated into separate piecewise linear segments (splines) of differing gradients.
Published in Chapter:
Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR
Jagan J. (VIT University, India), Pijush Samui (National Institute of Technology Patna, India), and Barnali Dixon (University of South Florida St. Petersburg, USA)
DOI: 10.4018/978-1-4666-9723-2.ch001
Abstract
The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.
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