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Learning classifier system
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Learning classifier system - Wikipedia
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The Difference Campaign Campaign News. Alumni Call Campaign. Current Students and Staff. When a classifier is selected for deletion, its numerosity parameter is reduced by one. When the numerosity of a classifier is reduced to zero, it is removed entirely from the population. LCS will cycle through these steps repeatedly for some user defined number of training iterations, or until some user defined termination criteria have been met.
For online learning, LCS will obtain a completely new training instance each iteration from the environment. For offline learning, LCS will iterate through a finite training dataset. Once it reaches the last instance in the dataset, it will go back to the first instance and cycle through the dataset again.belgacar.com/components/espionner/ecoute-telephonique-fixe.php
Once training is complete, the rule population will inevitably contain some poor, redundant and inexperienced rules. It is common to apply a rule compaction , or condensation heuristic as a post-processing step. This resulting compacted rule population is ready to be applied as a prediction model e.
Whether or not rule compaction has been applied, the output of an LCS algorithm is a population of classifiers which can be applied to making predictions on previously unseen instances. The prediction mechanism is not part of the supervised LCS learning cycle itself, however it would play an important role in a reinforcement learning LCS learning cycle. For now we consider how the prediction mechanism can be applied for making predictions to test data. When making predictions, the LCS learning components are deactivated so that the population does not continue to learn from incoming testing data.
A test instance is passed to [P] where a match set [M] is formed as usual. At this point the match set is differently passed to a prediction array. Rules in the match set can predict different actions, therefore a voting scheme is applied.
In a simple voting scheme, the action with the strongest supporting 'votes' from matching rules wins, and becomes the selected prediction. All rules do not get an equal vote.
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Rather the strength of the vote for a single rule is commonly proportional to its numerosity and fitness. This voting scheme and the nature of how LCS's store knowledge, suggests that LCS algorithms are implicitly ensemble learners. Rules that constitute the LCS prediction model can be ranked by different rule parameters and manually inspected. Global strategies to guide knowledge discovery using statistical and graphical have also been proposed. John Henry Holland was best known for his work popularizing genetic algorithms GA , through his ground-breaking book "Adaptation in Natural and Artificial Systems"  in and his formalization of Holland's schema theorem.
In , Holland conceptualized an extension of the GA concept to what he called a "cognitive system",  and provided the first detailed description of what would become known as the first learning classifier system in the paper "Cognitive Systems based on Adaptive Algorithms". This early, ambitious implementation was later regarded as overly complex, yielding inconsistent results. Beginning in , Kenneth de Jong and his student Stephen Smith took a different approach to rule-based machine learning with LS-1 , where learning was viewed as an offline optimization process rather than an online adaptation process.
Interest in learning classifier systems was reinvigorated in the mid s largely due to two events; the development of the Q-Learning algorithm  for reinforcement learning , and the introduction of significantly simplified Michigan-style LCS architectures by Stewart Wilson. ZCS demonstrated that a much simpler LCS architecture could perform as well as the original, more complex implementations.
However, ZCS still suffered from performance drawbacks including the proliferation of over-general classifiers.
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In , Wilson published his landmark paper, "Classifier fitness based on accuracy" in which he introduced the classifier system XCS. XCS was popularized by its ability to reach optimal performance while evolving accurate and maximally general classifiers as well as its impressive problem flexibility able to perform both reinforcement learning and supervised learning.