An experimental comparison of hybrid modified genetic algorithm-based prediction models

ISSN: 22773878
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Abstract

The quest for an optimal prediction model is still a hot topic in the field of data mining and machine learning. An optimal model is achieved when the algorithm used posses the highest performance rating based on the evaluation matrix the researchers sought to satisfy. Through this study, a hybrid modified genetic algorithm-based prediction was modeled along with the selected data mining algorithms namely the K-Nearest Neighbor, Naive Bayes, C4.5, and Rule Base algorithms such as DT, JRip, OneR, and PART. The crossover operator of the genetic algorithm was also modified to optimize the minimization process of the variables before prediction. The simulation results showed that the MGA-KNN outperformed the MGA-NB, MGA-C4.5 and MGA-RB with DT, JRip, OneR and PART algorithms with the prediction accuracy of 94%, 86%, 89%, 85%, 92%, 75%, and 92%, respectively.

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APA

Delima, A. J. P. (2019). An experimental comparison of hybrid modified genetic algorithm-based prediction models. International Journal of Recent Technology and Engineering, 8(1), 1756–1760.

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