Pattern Recognition Method Using Ensembles of Regularities Found by Optimal Partitioning

New pattern recognition method is considered that is based on ensembles of ”syndromes”. The developed method that is referred to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted Syndromes (SWS) method. ”Syndromes” are subregions in space of prognostic features where content of objects from one of the classes differs significantly from the same class contents in neighboring subregions. ”Syndromes” are discussed as simple basic classifiers that are combined with the help of weighted voting procedure. Method of optimal partitioning of input features space is used for ”syndromes” searching. At that ”syndromes” are selected depending on quality of data separation and complexity of used partitioning model (partitions family). Performance of MSWS is compared with performance of SWS and alternative techniques in several applied tasks. Influence of recognition ability on characteristics of ”syndromes” selection is studied.