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Optimal Combiners For Multiple Classifier Systems, Mohammed Falih Hassan

Dissertations

A Multiple Classifier System (MCS) is designed to combine classification results of an ensemble of different classifiers and consequently to produce the highest possible classification output. MCS has recently drawn growing attention and has become a necessity, especially when a problem involves a large class of noisy data or when using a single pattern classifier that has serious drawbacks in its results. A wide range of pattern recognition applications have benefited from the implementation of MCS, these include areas such as handwriting recognition, incremental learning, data fusion, feature selection, and a large variety of medical applications.

To achieve optimal ensemble …