The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number and large missing data. First, we compared removal imputation methods, method imputing was found be more effective. Then, in order recommend model, created total 126 models (missing imputation: single imputation, multiple imputations, K-nearest neighbor im...

This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The main contribution is...

The main purpose of the current research is comparing the results of Artificial Neural Network (ANN) with Adaptive Neuro-Fuzzy Inference System (ANFIS) with regard to determination of the importance of soil properties affecting clay dispersibility. After taking samples from two depths of 0-40 and 40-80 cm, the spontaneous and mechanical dispersions of clay were recorded using both weighing and ...

Artificial neural networks (ANNs) represent a powerful analytical tool designed for predictive modeling. However the shortage of straightforward and reliable approaches for calculating variable importance and characterizing predictor–response relationships has likely hindered the broader use of ANNs in ecology. Two such metrics – product-of-connection-weights (PCW) and product-of-standardized-w...

Applications of graphical models often require the use of approximate inference, such as sequential importance sampling (SIS), for estimation of the model distribution given partial evidence, i.e., the target distribution. However, when SIS proposal and target distributions are dissimilar, such procedures lead to biased estimates or require a prohibitive number of samples. We introduce ReBaSIS,...

The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is...