Neuronal Communication Genetic Algorithm-Based Inductive Learning

Neuronal Communication Genetic Algorithm-Based Inductive Learning

Abdiya Alaoui (EEDIS Laboratory, Department of Computer Science, Djillali Liabes University Sidi Belabbes, Algeria) and Zakaria Elberrichi (EEDIS Laboratory, Department of Computer Science, Djillali Liabes University Sidi Belabbes, Algeria)
Copyright: © 2020 |Pages: 14
DOI: 10.4018/JITR.2020040109
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Abstract

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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