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In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.<\/jats:p>","DOI":"10.3233\/ida-205494","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T20:07:21Z","timestamp":1635883641000},"page":"1547-1563","source":"Crossref","is-referenced-by-count":7,"title":["MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm"],"prefix":"10.1177","volume":"25","author":[{"given":"Paria","family":"Golshanrad","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran"}]},{"given":"Hossein","family":"Rahmani","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran"},{"name":"Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands"}]},{"given":"Banafsheh","family":"Karimian","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran"}]},{"given":"Fatemeh","family":"Karimkhani","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran"}]},{"given":"Gerhard","family":"Weiss","sequence":"additional","affiliation":[{"name":"Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205494_ref1","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis","year":"2007","journal-title":"Emerging Artificial Intelligence Applications in Computer Engineering"},{"key":"10.3233\/IDA-205494_ref2","doi-asserted-by":"crossref","unstructured":"J.R. 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