Archive for the ‘Classifier Fusion’ Category

Combining Pattern Classifiers: Methods and Algorithms

Saturday, March 12th, 2011

Combining Pattern Classifiers: Methods and Algorithms, Ludmila I. Kuncheva (2004)

WorldCat entry: Combining Pattern Classifiers: Methods and Algorithms

From the preface:

Everyday life throws at us an endless number of pattern recognition problems: smells, images, voices, faces, situations, and so on. Most of these problems we solve at a sensory level or intuitively, without an explicit method or algorithm. As soon as we are able to provide an algorithm the problem becomes trivial and we happily delegate it to the computer. Indeed, machines have confidently replaced humans in many formerly difficult or impossible, now just tedious pattern recognition tasks such as mail sorting, medical test reading, military target recognition, signature verification, meteorological forecasting, DNA matching, fingerprint recognition, and so on.

In the past, pattern recognition focused on designing single classifiers. This book is about combining the “opinions” of an ensemble of pattern classifiers in the hope that the new opinion will be better than the individual ones. “Vox populi, vox Dei.”

The field of combining classifiers is like a teenager: full of energy, enthusiasm, spontaneity, and confusion; undergoing quick changes and obstructing the attempts to bring some order to its cluttered box of accessories. When I started writing this book, the field was small and tidy, but it has grown so rapidly that I am faced with the Herculean task of cutting out a (hopefully) useful piece of this rich, dynamic, and loosely structured discipline. This will explain why some methods and algorithms are only sketched, mentioned, or even left out and why there is a chapter called “Miscellanea” containing a collection of important topics that I could not fit anywhere else.

Appreciate the author’s suggesting of older material to see how the pattern recognition developed.

Suggestions/comments on this or later literature on pattern recognition?

Ensemble Based Systems in Decision Making

Friday, November 26th, 2010

Ensemble Based Systems in Decision Making Authors: Robi Polikar Keywords: Multiple classifier systems, classifier combination, classifier fusion, classifier selection, classifier diversity, incremental learning, data fusion


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

Ironic that the second paragraph of the abstract starts off with the very semantic diversity that bedevils effective information retrieval and navigation.

Excellent survey article on ensemble systems.


  1. Read and summarize this article. (1-2 pages)
  2. Choose a data set (list to be posted for class). Outline the choices or evaluations you would make in assembling an ensemble system. (3-5 pages, no citations)
  3. Build an ensemble system to assist with building a topic map for a specific data set (Project)