Neuronal Communication Genetic Algorithm-Based Inductive Learning

Neuronal Communication Genetic Algorithm-Based Inductive Learning

Abdiya Alaoui, Zakaria Elberrichi
Copyright: © 2020 |Pages: 14
DOI: 10.4018/JITR.2020040109
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The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.
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This section explores the research works which are related to the proposed hybrid approach.

The authors (Alberto et al., 2010) give an exhaustive survey of the genetics-based machine learning (GBML) Algorithms for rule induction, an experimental analysis for classification is shown in this article. A comparative study of the GBML algorithms with other non-evolutionary algorithms is presented, the evolutionary algorithms (EA) give a competitive results compared to a classical machine learning algorithms (CART analysis (Breiman et al., 1984), AQ (Michalksi et al., 1986), CN2 (Clark & Niblett, 1989), C4.5 (Quinlan, 1993), C4.5-Rules (Quinlan, 1995), Ripper (Cohen, 1995) in terms of classification accuracy, those algorithms with their references are shown in Table 1

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