Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we ...

This paper presents a supervised feature selection method applied to regression problems. The selection method uses a Dissimilarity matrix originally developed for classification problems, whose applicability is extended here to regression and built using the conditional mutual information between features with respect to a continuous relevant variable that represents the regression function. A...

Premature Chromosome Condensation (PCC) appears to have a possible utility for biological dosimetry purposes. The PCC technique may be adapted for cases of suspicion of overexposure where sampling is performed at least one day after an accident. For this purpose, human blood samples were exposed in vitro to 60Co up to 10 Gy and the PCC technique was performed immediately after irradiation. Anal...

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (l1-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, w...

In this paper we adapt the recently proposed Dynamic Integration ensemble techniques for regression problems and compare their performance to the base models and to the popular ensemble technique of Stacked Regression. We show that the Dynamic Integration techniques are as effective for regression as Stacked Regression when the base models are simple. In addition, we demonstrate an extension to...

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (L1-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, w...

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily C...

As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...

In this paper we continue study on the Stepwise Adaptation of Weights (saw) technique. Previous studies on constraint satisfaction and data classification have indicated that saw is a promising technique to boost the performance of evolutionary algorithms. Here we use saw to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance o...

This paper demonstrates the use of regression tree models to predict the number of faults in a software module based on the software complexity metrics, prior to the testing phase, which can help in channel-ing the validation and testing eeorts in a productive direction. We also compare the regression tree model-ing technique with the fault density technique which is a very commonly used approa...