MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

P. Golshanrad, H. Rahmani*, B. Karimian, F. Karimkhani, G. Weiss

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. 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.
Original languageEnglish
Pages (from-to)1547-1563
Number of pages17
JournalIntelligent Data Analysis
Issue number6
Publication statusPublished - 2021


  • Ensemble methods
  • meta-learning
  • genetic algorithm
  • meta-feature


Dive into the research topics of 'MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm'. Together they form a unique fingerprint.

Cite this