Learning and programming in classifier systems
نویسندگان
چکیده
منابع مشابه
NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...
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Genetic Algorithms has given rise to two new fields of research where (global) optimisation is of crucial importance: ‘genetic based machine learning’ (GBML) and ‘genetic programming’ (GP). An advanced implementation of GBML (Fuzzy Efficiency based Classifier System, FECS, developed by the authors) and GP (as defined by Koza) are both applied to the case study ‘fibre-to-yarn production process’...
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designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing fuzzy learning classifier (flc) systems. conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. in thispaper new entities namely precision and recall from the field of information retrieval (ir)systems is adapted as alternative...
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Genetics-based machine learning methods also called learning classifier systems are evolutionary computation based data mining techniques. The advantages of these techniques are: they are rule-based models providing human-readable learning patterns; they are incremental learners allowing the system to adapt quickly in dynamic environments; and some of them have linear 0(n) learning complexity i...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1988
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00113897