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A new game-theoretic approach for combining multiple classifiers is proposed.A short introduction in Game Theory and coalitions illustrate the way anycollective decision scheme can be viewed as a competitive game of coalitionsthat are formed naturally when players state their preferences. The winningconditions and the voting power of each player are studied under the scope ofvoting power indices, as well and the collective competence of the group.Coalitions and power indices are presented in relation to the Condorcetcriterion of optimality in voting systems, and weighted Borda count models areasserted as a way to implement them in practice. A special case of coalitiongames, the weighted majority games (WMG) are presented as a restrictedrealization in dichotomy choice situations. As a result, the weighted majorityrules (WMR), an extended version of the simple majority rules, are asserted asthe theoretically optimal and complete solution to this type of coalitiongaming. Subsequently, a generalized version of WMRs is suggested as the meansto design a voting system that is optimal in the sense of both the correctclassification criterion and the Condorcet efficiency criterion. In the scopeof Pattern Recognition, a generalized risk-based approach is proposed as theframework upon which any classifier combination scheme can be applied. A newfully adaptive version of WMRs is proposed as a statistically invariant way ofadjusting the design process of the optimal WMR to the arbitrarynon-symmetrical properties of the underlying feature space. SVM theory isassociated with properties and conclusions that emerge from the game-theoreticapproach of the classification in general, while the theoretical and practicalimplications of employing SVM experts in WMR combination schemes are brieflydiscussed. Finally, a summary of the most important issues for further researchis presented.

Collective decision efficiency and optimal voting mechanisms: A comprehensive overview for multi-classifier models