COMBINING CLASSIFIERS: SOFT COMPUTING SOLUTIONS
Ludmila Ilieva Kuncheva · Pattern Recognition · 2001
Abstract ∗ Classifier combination is now an established pattern recognition subdiscipline. Despite the strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and empirical solutions. Assuming that “soft computing ” encompasses neural networks, evolutionary computation, and fuzzy sets, we explain how each of the three components has been used in classifier combination. Let D = {D1, D2,..., DL} be a set of classifiers (we shall also call D a team or ensemble), and let Ω = {ω1,..., ωc} be a set of class labels. Each classifier gets as its input a feature vector x = [x1,..., xn] T, x ∈ ℜ n and assigns it to a class label from Ω, i.e., Di: ℜ n → Ω. Alternatively, we may define the classifier