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An evolutionary framework for machine learning applied to medical data
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DOI：10.1016/j.knosys.2019.104982.png)
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Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Machine learning
Logical rule induction
Data mining
Supervised learning
Evolutionary computation
Genetic programming
Ensemble classifier
Medical data
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