Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Investigation of a Novel Self-configurable Multiple Classifier System for Character Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Trainable fusion rules. II. Small sample-size effects
Neural Networks
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Key generation in a voice based template free biometric security system
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
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In this paper we propose two novel multiple classifier fusion schemes which, although different in terms of architecture, share the idea of dynamically extracting additional statistical information about the individually trained participant classifiers by reinterpreting their outputs on a validation set. This is achieved through training on the resulting intermediate feature spaces of another classifier, be it a combiner or an intermediate stage classification device. We subsequently implemented our proposals as multi-classifier systems for handwritten character recognition and compare the performance obtained through a series of cross-validation experiments of increasing difficulty. Our findings strongly suggest that both schemes can successfully overcome the limitations imposed on fixed combination strategies from the requirement of comparable performance levels among their participant classifiers. In addition, the results presented demonstrate the significant gains achieved by our proposals in comparison withb oth individual classifiers experimentally optimized for the task in hand, and a multi-classifier system design process which incorporates artificial intelligence techniques.