{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T12:12:12Z","timestamp":1673352732306},"reference-count":44,"publisher":"MIT Press - Journals","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2014,12]]},"abstract":"<jats:p> 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\u2019s classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learned rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 [Formula: see text] 1% accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95%. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learned angles\u2014a normal distribution around [Formula: see text]\u2014which would have been very difficult in subsymbolic approaches. The analyzable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable. <\/jats:p>","DOI":"10.1162\/evco_a_00127","type":"journal-article","created":{"date-parts":[[2014,4,3]],"date-time":"2014-04-03T20:24:27Z","timestamp":1396556667000},"page":"629-650","source":"Crossref","is-referenced-by-count":6,"title":["Human-Interpretable Feature Pattern Classification System Using Learning Classifier Systems"],"prefix":"10.1162","volume":"22","author":[{"given":"Toktam","family":"Ebadi","sequence":"first","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignas","family":"Kukenys","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Will N.","family":"Browne","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.7.1545"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1162\/106365603322365289"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04020-7_57"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4409066"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.885163"},{"key":"B8","volume-title":"Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design.","author":"Butz M. 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