Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble method. Ensemble methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose improving stability and accuracy algorithms. This study presented REMARS (Random MARS), new MARS selection approach using Random Forest (RF) al...

The model of adaptive hinging hyperplanes (AHH) is proposed in this paper for black-box modeling. It is based on Multivariate Adaptive Regression Splines (MARS) and Generalized Hinging Hyperplanes (GHH) and shares attractive properties of the two. By making a modification to the basis function of MARS, AHH shows linear property in each subarea. It is proved that AHH model is identical to a spec...

Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we r...

In this paper, we first examine several volatility models in the literature. We then estimate financial volatility using multivariate adaptive regression splines (MARS) by logarithmic transformation as a preliminary analysis to examine a nonparametric volatility model. Despite its popularity, MARS has never been applied to model financial volatility. To implement the MARS methodology in a time ...

Future Mars rovers, such as the planned 2009 MSL rover, require sufficient autonomy to robustly approach rock targets and place an instrument in contact with them. It took the 1997 Sojourner Mars rover between 3 and 5 communications cycles to accomplish this on rocks. This paper describes the NASA Ames approach to robustly accomplishing single cycle instrument deployment, using the K9 prototype...

Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a spe...