DESIGN OF MULTIPLE CLASSIFIER SYSTEMS
Fabio Roli, Giorgio Giacinto · Series in machine perception and artificial intelligence · 2002
Introduction In the past decade, a number of papers 19,28 have proposed the combination of multiple classifiers for designing high performance pattern classification systems. The rationale behind the growing interest in multiple classifier systems (MCSs) is that the classical approach to designing a pattern recognition system, which focuses on the search for the best individual classifier, has some serious drawbacks. The main drawback is that the best individual classifier for the classification task at hand is very di#cult to identify, 199 200 F. Roli & G. Giacinto unless deep prior knowledge is available for such a task. 3,8 In addition, with a single classifier it is not possible to exploit the complementary discriminatory information that other classifiers may encapsulate. It is worth noting that the motivations in favour of MCS strongly resemble those of a "hybrid" intelligent system. 15,23 The obvious reason for this is that MCS can be regarded as a special-purpose hy