A stacked generalization data mining approach was applied to a simplified version of the KDD Cup 2001 protein localization task. Four level-0 models were developed: an Artificial Neural Network, a Decision Tree, a Nearest Neighbor Classifier, and a Hybrid model that trained an Artificial Neural Network using those inputs selected as important by the Decision Tree. These models were developed fr...

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current de...

The purpose of this study was to improve the accuracy rate of brain tissue classification in magnetic resonance (MR) imaging using a boosted decision tree segmentation algorithm. Herein, we examined simulated phantom MR (SPMR) images, simulated brain MR (SBMR) images, and a real data. The accuracy rate and k index when classifying brain tissues as gray matter (GM), white matter (WM), or cerebra...

The use of mobile phones has exploded over the past years, abundantly through the introduction of smartphones and the rapidly expanding use of mobile data. This has resulted in a spiraling problem of ensuring quality of service for users of mobile networks. Hence, mobile carriers and service providers need to determine how to prioritise expansion decisions and optimise network faults to ensure ...

Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. This paper proposes a method called “RBDT-1”rule based decision tree -for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. RBDT-1 method uses a set of declarative rules a...

as we know that with the help of Data mining techniques we can find out knowledge in terms of various characteristics and patterns. In this regard this paper presents finding out of anomalies/ outliers using various decision tree based classifiers viz. Best-first Decision Tree, Functional Tree, Logistic Model Tree, J48 and Random Forest decision tree. Three real world datasets has been used in ...

Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques. Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people re...

Approximating stochastic processes by scenario trees is important in decision analysis. In this paper we focus on improving the approximation quality of trees by smaller, tractable trees. In particular we propose and analyze an iterative algorithm to construct improved approximations: given a stochastic process in discrete time and starting with an arbitrary, approximating tree, the algorithm i...