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In this paper, we propose a nonlinear feature extraction method for regression problems to reduce the dimensionality of the input space. Previously, a feature extraction method LDAr, a regressional version of the linear discriminant analysis, was proposed. In this paper, LDAr is generalized to a non-linear discriminant analysis by using the so called kernel trick. The basic idea is to map the i...

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there currently no effective method for determining single value or landmark from set of annotations regression problems. We propose novel deriving labels problems that considers the performance and precision individual annotators when identifying eac...

Machine learning techniques have been used to develop many regression models make predictions based on experience and historical data. They might be singly or in ensembles. Single are either classification that use one technique, while ensemble combine various single models. To construct find the best model is very complex time-consuming, so this study develops a new platform, called intelligen...

Regression problems have been widely studied in machine learning literature resulting a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem how incorporate categorical features problems. Usually, feature encoders general enough cover both classification This lack specificity results underperforming models. In this pa...