Ensemble based systems in decision making

TLDR

Ensemble‑based systems, also known as multiple classifier systems or mixture of experts, aggregate multiple expert opinions to enhance decision quality and have recently been shown to outperform single‑expert approaches in automated decision making. This article reviews the design, implementation, and application of ensemble‑based systems, exploring when they surpass single classifiers, how to generate and combine their components, and outlining future research directions. The paper surveys popular ensemble algorithms such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts, along with combination rules like algebraic output fusion, voting, behavior knowledge space, and decision templates, and highlights applications in incremental learning, data fusion, feature selection, missing‑feature handling, confidence estimation, and error‑correcting output codes.

Abstract

In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a process in automated decision making applications have only recently been discovered by computational intelligence community. Also known under various other names, such as multiple classifier systems, committee of classifiers, or mixture of experts, ensemble based systems have shown to produce favorable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. Design, implementation and application of such systems are the main topics of this article. Specifically, this paper reviews conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined. We discuss popular ensemble based algorithms, such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts; as well as commonly used combination rules, including algebraic combination of outputs, voting based techniques, behavior knowledge space, and decision templates. Finally, we look at current and future research directions for novel applications of ensemble systems. Such applications include incremental learning, data fusion, feature selection, learning with missing features, confidence estimation, and error correcting output codes; all areas in which ensemble systems have shown great promise