Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Table Recognition Evaluation and Combination Methods
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Multimodal Person Recognition for Human-Vehicle Interaction
IEEE MultiMedia
Combining classifiers with particle swarms
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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This study covers weighted combination methodologies for multiple classifiers to improve classification accuracy. The classifiers are extended to produce class probability estimates besides their class label assignments to be able to combine them more efficiently. The leave-one-out training method is used and the results are combined using proposed weighted combination algorithms. The weights of the classifiers for the weighted classifier combination are determined based on the performance of the classifiers on the training phase. The classifiers and combination algorithms are evaluated using classical and proposed performance measures. It is found that the integration of the proposed reliability measure, improves the performance of classification. A sensitivity analysis shows that the proposed polynomial weight assignment applied with probability based combination is robust to choose classifiers for the classifier set and indicates a typical one to three percent consistent improvement compared to a single best classifier of the same set.