The decision tree classifier: Design and potential

Quantum decision tree classifier

We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can man...

Fault Detection and Location in DC Microgrids by Recurrent Neural Networks and Decision Tree Classifier

Microgrids have played an important role in distribution networks during recent years.  DC microgrids are very popular among researchers because of their benefits. Protection is one of the significant challenges in the way of microgrids progress. As a result, in this paper, a fault detection and location scheme for DC microgrids is proposed. Due to advances in Artificial Intelligence (AI) and s...

Automatic design of a decision tree classifier employing neural networks

Using Data Structure Properties in Decision Tree Classifier Design

This paper studies the techniques of performance enhancement for decision tree classifiers (DTC) that are based on data structure analysis. To improve the performance of DTC, two methods are used – class decomposition that uses the structure of class density and taxonomy based DTC design that uses interactions between attribute values. The paper shows experimental exploration of the methods, th...

VPRSM Based Decision Tree Classifier

A new approach for inducing decision trees is proposed based on the Variable Precision Rough Set Model. From the rough set theory point of view, in the process of inducing decision trees with evaluations of candidate attributes, some methods based on purity measurements, such as information entropy based methods, emphasize the effect of class distribution. The more unbalanced the class distribu...