Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Efficient load balancing for wide-area divide-and-conquer applications
PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
Robust automatic target recognition using learning classifier systems
Information Fusion
Fused, multi-spectral automatic target recognition with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Backpropagation applied to handwritten zip code recognition
Neural Computation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Supervised learning classifier systems for grid data mining
CIS'09 Proceedings of the international conference on Computational and information science 2009
E-SCIENCEW '10 Proceedings of the 2010 Sixth IEEE International Conference on e-Science Workshops
XCSF with local deletion: preventing detrimental forgetting
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Transparent, online image pattern classification using a learning classifier system
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Flexible, high performance convolutional neural networks for image classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
An abstract deep network for image classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Extracting features from images is an important task in order to identify (classify) the patterns contained. The Evolutionary Computation and Reinforcement Learning technique of Learning Classifier Systems (LCSs) has been successfully applied to classification tasks, but rarely to image pattern classification due to the large search space associated with pixel data. Recently, a Feature Pattern Classification System (FPCS), utilising Haar-like features has been introduced with promising results in the image recognition domain. This system used a confusion-matrix to direct learning to hard to classify classes, but due to its reinforcement learning nature was required to estimate the ground truth. The novel work presented here adopts a supervised learning (UCS-based) approach into the FPCS framework. This work is compared with the original XCS-based system, updated to include the known ground-truth of the confusion matrix to aid comparison, albeit no longer reinforcement learning. Results on the 10 class MNIST numerical digits recognition task show that the XCS-based FPCS produces better classification due to its complete mapping guiding learning. However, results on the 26 class NIST character recognition task show that the UCS-based scales better as it does not require the complete mapping. The human readable rules produced by each system, coupled with the competitive classification performance compared with similar techniques, supports future work on both the XCS and UCS-based FPCS.