Optimal combinations of pattern classifiers
Pattern Recognition Letters
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new hybrid approach in combining multiple experts to recognise handwritten numerals
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for Weighted Combination of Classifiers Employing Shared and Distinct Representations
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Classifier Combination as a Tomographic Process
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
On the General Application of the Tomographic Classifier Fusion Methodology
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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We propose a new method for performance-constraining the feature selection process as it relates to combined classifiers, and assert that the resulting technique provides an alternative to the more familiar optimisation methodology of weight adjustment. The procedure then broadly involves the prior selection of features via performance-constrained sequential forward selection applied to the classifiers individually, with a subsequent forward selection process applied to the classifiers acting in combination, the selection criterion in the latter case deriving from the combined classification performance. We also provide a number of parallel investigations to indicate the performance enhancement expected of the technique, including an exhaustive weight optimisation procedure of the customary type, as well as an alternative backward selection technique applied to the individually optimised feature sets.