A feature-based serial approach to classifier combination

A new approach to the serial multi-stage combination of classifiers is proposed. Each classifier in the sequence uses a smaller subset of features than the subsequent classifier. The classification provided by a classifier is rejected only if its decision is below a predefined confidence level. The approach is tested on a two-stage combination of k-Nearest Neighbour classifiers.. The features to be used by the first classifier in the combination are selected by two stand-alone algorithms (Relief and Info-Fuzzy Network, or IFN) and a hybrid method, called 'IFN + Relief. The feature-based approach is shown empirically to provide a substantial decrease in the computational complexity, while maintaining the accuracy level of a single-stage classifier or even improving it.