Combining Pattern Classifiers: Methods and Algorithms (Kuncheva, L.I.; 2004) [book review]

This book, which is wholly devoted to the subject of model combination, is divided into ten chapters. In addition to the first two introductory chapters, the book covers some of the following topics: multiple classifier systems; combination methods when the base classifier outputs are 0/1; methods when the outputs are continuous, e.g., posterior probabilities; methods for classifier selection; bagging and boosting; the theory of fixed combination rules; and the concept of diversity. Overall, it is a very well-written monograph. It explains and analyzes different approaches comparatively so that the reader can see how they are similar and how they differ. The literature survey is extensive. The MATLAB code for many methods is given in chapter appendices allowing readers to play with the explained methods or apply them quickly to their own data. The book is a must-read for researchers and practitioners alike.