in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

A decision tree (DTs) is one of the most popular machine learning algorithms that divide data repeatedly to form groups or classes. It a supervised algorithm can be used on discrete continuous for classification regression. The traditional classifier in this C4.5 tree, which point research. This has advantage building vast set and does not stop until it reaches desired goal. problem with there ...

To avoid checking unnecessary or irrelevant conditions of rules, the irrelevant values problem of the decision tree is addressed. We propose an algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to the semantics of the decision tree. Since our algorithm depends only on the semantics of the decision tree, our algorithm can be inte...

introduction: the aim of this study was to determine the performance of data mining techniques for predicting the causes of traumatic brain injuries in khatamolanbya hospital, zahdan city. method: in this cross–sectional, the study population included all patients who died of brain injury. data were collected by the use of a researcher- made check list, provided under the direct observation of ...

background : breast cancer survival has been analyzed by many standard data mining algorithms. a group of these algorithms belonged to the decision tree category. ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied...