machine learning algorithms Multivariate adaptive regression
Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: evidence from Taiwan
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In the past few decades, international tourism has grown rapidly and has become a very interesting topic in tourism research. Taiwan, acting as a citizen in the global community, improved traveling facilities, and governments strong promotion has drawn more and more
Rainfall forecasting using soft computing models and multivariate adaptive regression splines
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Long-term rainfall prediction is a challenging task especially in the modern world where we are facing the major environmental problem of global warming. In general, climate and rainfall are highly non-linear phenomena in nature exhibiting what is known as the butterfly
A HYBRID DEVICE OF SELF ORGANIZING MAPS (SOM) AND MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) FOR THE FORECASTING OF
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This paper proposes a hybrid approach to the forecasting of firms bankruptcy of Spanish enterprises from the construction sector. Our proposal starts splitting the group of healthy companies into two subgroups: borderline and non-borderline companies. Borderline
Analysis of freeway accident frequency using multivariate adaptive regression splines
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This study applies nonparametric multivariate adaptive regression splines (MARS) modeling technique to explore the effects of nonbehavior factors including highway geometrics characteristics, traffic factors as well as environmental conditions on freeway accidents
A novel method for disease prediction: hybrid of random forest and multivariate adaptive regression splines
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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
Tourism demand forecasting: Econometric model based on multivariate adaptive regression splines, artificial neural network and support vector regression
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This paper develops tourism demand econometric models based on the monthly data of tourists to Taiwan and adopts Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR), MARS, ANN and SVR to
Profiling poverty with multivariate adaptive regression splines
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The PIDS Discussion Paper Series constitutes studies that are preliminary and subject to further revisions. They are be- ing circulated in a limited number of cop- ies only for purposes of soliciting com- ments and suggestions for further refine- ments. The studies under the Series are unedited
INVITED PAPER MULTIVARIATE ADAPTIVE REGRESSION SPLINES
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A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and
Data mining in real estate appraisal: a model tree and multivariate adaptive regression spline approach
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In this paper we adopt two exploratory modelling techniques: Model Trees and Multivariate Adaptive Regression Splines. The objective is the building of two sale price prediction models in order to highlight possible market segments not detectable a priori. We show how
Feature selection using multivariate adaptive regression splines
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Multi-label learning is a supervised learning method in which classification algorithm is required to learn a set of instances; where each instance belongs to multiple classes. Feature selection in the multi-label dataset is a challenging task due to complex interaction
Application of multivariate adaptive regression splines to evaporation losses in reservoirs
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Abstract This study adopts Multivariate Adaptive Regression Splines (MARS) for prediction of Evaporation Losses (E) in reservoirs. MARS is a technique to estimate general functions of highdimensional arguments given sparse data. The input variables of MARS model are
Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs)
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Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines
Multivariate convex regression with adaptive partitioning
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We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there
Comparison of multivariate adaptive regression splines and random forest regression in predicting forced expiratory volume in one second
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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and
Convex version of multivariate adaptive regression splines
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Multivariate adpative regression splines (MARS) provide a flexible statistical modeling method that employs forward and backward search algorithms to identify the combination of basis functions that best fits the data and simultaneously conduct variable selection. In
Heart Rate Detection using Independent Component Analysis and Multivariate Adaptive Regression Splines
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In this paper we describe a heart rate detection system that improves up on the existing heart rate detection system that uses independent component analysis by adding a regression system. The system has been trained using multivariate adaptive regression
Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams
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This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical
An introduction to multivariate adaptive regression splines for the cane industry
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INDUSTRIES strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties
Survival analysis with multivariate adaptive regression splines using cox-snell residual
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Multivariate Adaptive Regression Splines (MARS) are a generalization of stepwise linear regression method that is often employed to improve the efficiency of regression models. It is a useful tool to identify linear/nonlinear and interactions effects between a set of metrical and
Using multivariate adaptive regression spline and artificial neural network to simulate urbanization in Mumbai, India
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Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a