ABSTRACT
Learning Classifier Systems (LCS) have not been widely applied to image recognition tasks due to the very large search space of pixel data. Assimilating the image domain's Haar-like features into the XCS framework, the feature pattern classifier system (FPCS) has produced promising results in the numeral recognition task. However for large multi-class image classification problems the training rates can be unacceptably slow, whilst performance does not match supervised learning approaches. This is partially due to the fact that traditional LCS only retain limited information about the problem examples. Confusion Matrices show the classes that a learning technique has difficulty separating, but require supervised knowledge. This paper shows that the knowledge in a confusion matrix is beneficial in directing learning. Most importantly the work shows that confusion matrices can be beneficially adapted to non-supervisory learning.
- M. V. Butz. Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design. Springer Verlag, Berlin Heidelberg, 2006.Google Scholar
- I. Kukenys, W. N. Browne, and M. Zhang. Transparent, Online Image Pattern Classification Using a Learning Classifier System. In European Conference on the Applications of Evolutionary Computation, 27-29 April 2011. Google ScholarDigital Library
- P. L. Lanzi and A. Perrucci. Extending the representation of classifier conditions part ii: From messy coding to s-expressions. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 345--352, 1999.Google Scholar
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11):2278--2324, 1998.Google ScholarCross Ref
- P. Viola and M. Jones. Rapid Object Detection Using a Boosted Cascade of Simple Features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, 2001.Google ScholarCross Ref
- S. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2):149--175, 1995. Google ScholarDigital Library
Index Terms
- Confusion matrices for improving performance of feature pattern classifier systems
Recommendations
Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rules
Learning classifier systems (LCSs), an established evolutionary computation technique, are over 30 years old with much empirical testing and foundations of theoretical understanding. XCS is a well-tested LCS model that generates optimal (i.e., maximally ...
Human-interpretable feature pattern classification system using learning classifier systems
Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches have proven accurate in learning a problem's classes. However, in the complex image recognition domain, there is a need ...
Intrusion detection with evolutionary learning classifier systems
Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy ...
Comments