This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace...

Abstract Ensemble hydrologic forecasting which takes advantages of multiple models has made much contribution to water resource management. In this study, four hydrological (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) three ensemble methods simple average, black box-based, binomial-based methods) were applied compared simulate the process during 1979–1...

Background: Breast cancer is the second leading cause of cancer death in women, after lung cancer. Due to the importance of predicting this disease, the use of data mining methods in medical research is more significant than before. Data mining algorithms can be a great help in preventing the development of lymphedema in patients. The aim Of this study was to create a diagnosis system that can ...

Abstract We investigate the application of ensemble transform approaches to Bayesian inference logistic regression problems. Our approach relies on appropriate extensions popular Kalman filter and feedback particle cross entropy loss function is based a well-established homotopy inference. The arising finite evolution equations as well their mean-field limits are affine-invariant. Furthermore, ...