An Experimental Comparison of Hybrid Modified Genetic Algorithm-based Prediction Models
Allemar Jhone P. Delima
Allemar Jhone P. Delima, College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines.
Manuscript received on 09 April 2019 | Revised Manuscript received on 14 May 2019 | Manuscript published on 30 May 2019 | PP: 1756-1760 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1851058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Hybrid Prediction Model, Modified Genetic Algorithm, IBAX Operator, Prediction Accuracy Enhancement
Scope of the Article: Algorithm Engineering