Feature Selection for Serving Medical Datasets Applying Heuristic Algorithms (Scatter Search within Decision Tree Classifier)
DOI:
https://doi.org/10.33977/2106-000-008-002Keywords:
metaheuristic (MH), feature selection (FS), scatter search Algorithm (SSA), descision tree (DT), medical datasetsAbstract
Objectives: This research presents a feature selection process on different datasets of the medical domain with different aims and sizes using a wrapper approach based on a powerful metaheuristic algorithm which is the Scatter Search Algorithm and J48 decision tree classifier as the selection criteria.
Methods: The paper applied a modified approach of the basic Sequential Scatter Search algorithm called Improved Sequential Scatter Search follows the basic procedures of the original algorithm in addition to an early improvement mechanism choosing decision tree classifier to be the evaluator of the experiments.
Results: The experimental results show competition and superiority in feature selection compared to other metaheuristic algorithms for the same datasets in consideration of number of features selected and accuracy.
Conclusion: This research emphasizes the importance of wrapper approaches using metaheuristic algorithms to select the most dominant attributes in a dataset which is very important in reduction of the cost and complexity of all data analysis areas.
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