IEEE

Decision Trees for Mining Data Streams Based on the Gaussian Approximation

Abstract: Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision ...
Scientific Research Publishing

Application of Decision Tree Algorithm in Housing Purchase Problems

Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, ...
GitHub

MLHAT: Multi-Label Hoeffding Adaptive Trees for classification in multi-label data streams

Moreover, all of then are available ready to use in this framework under the folder src/ml_datasets. The datasets employed are the following, and more information about how to load them is presented ...
SciELO

Blood and peritoneal lactate in equine colic: application in emergency care and construction of a decision tree

The objective of this retrospective study was to determine whether blood lactate (BL) and peritoneal lactate (PL) are correlated with heart rate, BL-to-PL ratio and dehydration severity in horses with ...
GitHub

Awesome Decision, Classification, and Regression Tree Research Papers

Les E. Atlas, Ronald A. Cole, Jerome T. Connor, Mohamed A. El-Sharkawi, Robert J. Marks II, Yeshwant K. Muthusamy, Etienne Barnard [Paper] ...
IEEE

Decision Trees for Mining Data Streams Based on the McDiarmid's Bound

Abstract: In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a ...
Frontiers

Decision trees: from efficient prediction to responsible AI

This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, ...