When solving large and complex problems many of the existing monothetic models don't scale up. Research on cooperative, intelligent systems, involves studying, developing and evaluating architectures and methods to solve complex problems using adaptive and cooperative systems. These systems may range from simple software modules (such as a clustering or a classification algorithm) to physical systems (such as autonomous robots, machines or sensors). The main characteristic of these systems is that they are adaptive and cooperative. By adaptive, it is meant that the systems have a learning ability that makes them adjust their behaviour or performance to cope with changing situations. The systems are willing to cooperate together to solve complex problems or to achieve common goals. In this talk, I review multiple classifiers decision combining methods and categorize them in terms of their dependence on the input data as well as their ability to share data and decisions. I will explain different fusion architectures, modes of interactions among classifier ensembles and modules and strategies for combining the decisions. Performance of these architectures and implications on their implementations will be presented. I will also discuss the application of these models to a number of classification problems.

Sponsored by IEEE Canada and the Sections of Central Canada
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