MetaRecall: An Ensemble Classifier with Dynamic Base Classifier Selection and Ordering

Azarshab, Behnam; Ardeshiri, Sanaz; Rostami, Sanaz

doi:10.22034/jcse.2025.564479.1070

MetaRecall: An Ensemble Classifier with Dynamic Base Classifier Selection and Ordering

Document Type : Original Article

Authors

Behnam Azarshab ¹

Sanaz Ardeshiri ¹

Sanaz Rostami ²

¹ Address: Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Bushehr, 7516913817, Iran

² Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Bushehr, 7516913817, Iran

10.22034/jcse.2025.564479.1070

Abstract

The main criteria to evaluate different classifiers are accuracy of classification, time taken to build the classifier and classification

time as well as generalizability of the classifier. In this paper, we propose a novel ensemble classifier, named MetaRecall, which

exploits confusion matrix to automatically select its base classifiers to increase accuracy of ultimate classification. To do so,

a set of base classifiers as well as the training data set is fed to the algorithm as its input and the output of the algorithm is a

ensemble classifier which contains a subset of the given base classifiers. Each involved classifier in MetaRecall corresponds to a

class in the given dataset and its task is to classify instances of its corresponding class. To evaluate performance of MetaRecall,

we do extensive experiments on different well-known benchmark datasets. In addition, we compare MetaRecall with the most

commonly used previous ensemble classifiers. The results show that MetaRecall outperforms the previous classifiers in terms of

accuracy and execution time in many cases.

Keywords

Classification

Ensemble Classifier

Multiple Classifier System

Confusion Matrix

Automatic Base Classifier Selection