Combining Pattern Classifiers: Methods and Algorithms

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John Wiley & Sons, Aug 20, 2004 - Technology & Engineering - 300 pages
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A unified, coherent, and expansive treatment of current classifier ensemble methods

Mail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are just a few of the areas requiring reliable, precise pattern recognition.

Although in the past, pattern recognition has focused on designing single classifiers, recently the focus has been on combining several classifiers and getting a consensus of results for greater accuracy. This interest in combining classifiers has grown astronomically in recent years, evolving into a rich and dynamic, if loosely structured, discipline. Combining Pattern Classifiers: Methods and Algorithms represents the first attempt to provide a comprehensive survey of this fast-growing field. In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art.

Covering a broad range of methodologies, algorithms, and theories, the text addresses such questions as: Why should we combine classifiers?What are the current approaches for building classifier ensembles?What fusion methods can we use?How do we measure diversity in a classifier ensemble and is diversity really a key factor to its success?

Replete with case studies and real-world applications, this groundbreaking text will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones.

  

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Recently I've read the book Combining Pattern Classifiers, Methods and Algorithms by Ludmila I Kuncheva. The book addresses the problem of using multiple pattern classifiers to enhance the classification task in quantitative (e.g. accuracy) and qualitative (e.g. robustness) directions.
After taking three courses on pattern recognition and machine learning, the book helped me to get an idea over the whole subject of pattern analysis and classification. It starts with a compact but rich introduction to the theme of pattern recognition and the basic classifier types. Classifier design methods are studied in classifier vs. boundary design and parametric vs non-parametric learning dimensions. After the two introductory chapters on the discipline of pattern recognition, the concept of multi-classifier systems is introduced in chapter three. It is clarified in this chapter that the book is dedicated mainly to combining classifiers at the decision level and topics such as using diverse set of base classifiers are not discussed intensively. Then, combining of classifier outputs are treated in label output and continuous output categories. Various algorithms for majority voting and fusion of the label outputs are studied and compared throughout chapters three and four. Fusion of continuous valued outputs are studied in chapter five.
Selecting different classifiers for different sections of the input spaces are introduced in chapter six and the well-known knn method with its variations are studied in this chapter. Bagging and boosting methods for classifier selection are the subject of the seventh chapter.
The two stages before and after classifier combination; i.e. feature selection as the preprocessing and error corrections as the post-processing; discussed in chapter eight. The importance of feature selection and feature space partitioning are analyzed and demonstrated through the representative examples.
Most of the topics covered so far in the book were based on the results of the experiments in different application areas. Chapters nine and ten contain theoretical views and analysis on the classifier combination methods and rules.
Chapter ten is totally devoted to the diversity of classifiers in an ensemble. Diversity of the classifiers are studied in simple observable methods (e.g. training on different sections of input spaces) as well as statistical analysis tools and methods. Some open research directions are mentioned in the field of classifier combination.
As a student, I found this book helpful for gaining an understanding of the nature of pattern classifiers and the combination architectures. I'll recommend the book for those who took the elementary courses such as statistical pattern recognition and machine learning. The book will serve as a guiding resource for combining and using various familiar methods and algorithms.
 

Contents

1 Fundamentals of Pattern Recognition
1
2 Base Classifiers
45
3 Multiple Classifier Systems
101
4 Fusion of Label Outputs
111
5 Fusion of ContinuousValued Outputs
151
6 Classifier Selection
189
7 Bagging and Boosting
203
8 Miscellanea
237
9 Theoretical Views and Results
267
10 Diversity in Classifier Ensembles
295
References
329
Index
347
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About the author (2004)

LUDMILA I. KUNCHEVA is a Senior Lecturer in the School of Informatics at the University of Wales, Bangor, UK.

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