For classi cation problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classi cation problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that hav...

Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncertainty, especially the cognitive one. In this paper, we develop a method to adapt the decision tree technique to the case where the object’s classes are not exactly known, and where the uncertainty about the class’ value is repr...

Introduction Manual rating of specific risks begin with a base rate, which is then modified by appropriate relativity factors depending on characteristics of each risk. Classical methods of deriving indicated relativities, are described by McClenahan (1996) and Finger (1996). A number of different modeling procedures are described in Brown's (1988) "minimum bias" paper and Venter's (1990) revie...

Abstract The article discusses a method to control seepage in shafts. A special shaft model was built for this purpose. paper mainly focuses on electrical impedance tomography with image reconstruction where the machine learning used, then results were compared and different numerical models applied. key parameters are speed of analysis accuracy reconstructed objects. Applications most often pr...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...