An ensemble learning approach based on decision trees and probabilistic argumentation

Abstract

This research discusses a decision support system that includes different machine learning approaches (e.g. ensemble learning, decision trees) and a symbolic reasoning approach (e.g. argumentation). The purpose of this study is to define an ensemble learning algorithm based on formal argumentation and decision trees. Using a decision tree algorithmas a base learning algorithm and an argumentation framework as a decision fusion technique of an ensemble architecture, the proposed system produces outcomes. The introduced algorithm is a hybrid ensemble learning approach based on a formal argumentation-based method. It is evaluated with sample data sets (e.g. an open-access data set and an extracted data set from ultrasound images) and it provides satisfactory outcomes. This study approaches the problem that is related to an ensemble learning algorithm and a formal argumentation approach. A probabilistic argumentation framework is implemented as a decision fusion in an ensemble learning approach. An open-access library is also developed for the user. The generic version of the library can be used in different purposes