A survey of multiple classifier systems as hybrid systems <s> Ensuring diversity <s> In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier. Systems are effective only if the classifiers forming them make independent errors. Therefore, the fundamental need for methods aimed to design "error-independent" classifiers is currently acknowledged. In the paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting the subset formed by the most error-independent classifiers. Reported results on the classification of multisensor remote-sensing images show that this approach allows to design effective multiple classifier systems. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Ensuring diversity <s> 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. <s> BIB002
According to BIB002 BIB001 we can enforce the diversity of a classifier pool by the manipulation of either individual classifier inputs, outputs, or models.