Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach

A Novel Method for Disease Prediction: Hybrid of Random Forest and Multivariate Adaptive Regression Splines

Using data mining technology for disease prediction and diagnosis has become the focus of attention. Data mining technology provides an important means for extracting valuable medical rules hidden in medical data and acts as an important role in disease prediction and clinical diagnosis. This paper surveys some kind of popular data mining techniques for disease prediction and diagnosis, such as...

ESTIMATING DRYING SHRINKAGE OF CONCRETE USING A MULTIVARIATE ADAPTIVE REGRESSION SPLINES APPROACH

In the present study, the multivariate adaptive regression splines (MARS) technique is employed to estimate the drying shrinkage of concrete. To this purpose, a very big database (RILEM Data Bank) from different experimental studies is used. Several effective parameters such as the age of onset of shrinkage measurement, age at start of drying, the ratio of the volume of the sample on its drying...

Multivariate Adaptive Regression Splines

Discussion: Multivariate Adaptive Regression Splines

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Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines

This study proposes a rain rate retrieval algorithm for conicalscanning microwave imagers (RAMARS), as an alternative of the NASA Goddard Profiling (GPROF) algorithm, that does not rely on any a priory information. The fundamental basis of the RAMARS follows the concept of the GPROF algorithm, which means, being consistent with the TRMM PR rain rate observations, but independent of any auxiliar...