Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning
Francesco Molà, Claudio Conversano · 2008
A method called Sequential Automatic Search of a Subset of Classifiers is hereby introduced to deal with classification problems requiring decisions among a wide set of competing classes. It utilizes classifiers in a sequential way by restricting the number of competing classes while maintaining the presence of the true (class) outcome in the candidate set of classes. Some features of the method are discussed, namely: a cross-validation-based criteria to select the best classifier in each iteration of the algorithm, the resulting classification model and the possibility of choosing between an heuristic or probabilistic criteria to predict test set observations. Furthermore, the possibility to cast the whole method in the framework of unsupervised learning is also investigated. Advantages of the method are illustrated analyzing data from a letter recognition experiment.