Yousef Abdi

Dear Authors, My comments related to this work are as follows: * Please ensure that the language used in the figures presented is consistent with the context of the paper, which is in English. * It would be beneficial if you could provide informa...

Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For this reason, researchers have been paid attention and have proposed many methods to deal with this problem, which can be broadly categorized into data level and algorithm level. Besides, multi-class imbalanced learning is much harder than binary one and is still an open problem. Boosting algorithms are a class of ensemble learning methods in machine learning that improves the performance of separate base learners by combining them into a composite whole. This paper’s aim is to review the most significant published boosting techniques on multi-class imbalanced datasets. A thorough empirical comparison is conducted to analyze the performance of binary and multi-class boosting algorithms on various multi-class imbalanced datasets. In addition, based on the obtained results for performance evaluation metrics and a recently proposed criteria for comparing metrics, the selected metrics are compared to determine a suitable performance metric for multi-class imbalanced datasets. The experimental studies show that the CatBoost and LogitBoost algorithms are superior to other boosting algorithms on multi-class imbalanced conventional and big datasets, respectively. Furthermore, the MMCC is a better evaluation metric than the MAUC and G-mean in multi-class imbalanced data domains.

In the last decade, many of the metaheuristic search methods have been proposed for solving tough optimization problems. Each of these algorithms uses its own learn-by-example mechanism in terms of “movement strategy” to evolve the candidate solutions. In this paper, a framework, called Search Manager, is proposed for hybridizing different learn-by-example methods in one algorithm, which is inspired by the organizational management system in which managers change their management method by viewing performance reduction in their managerial organization. The proposed framework is verified using standard benchmark functions and real-world optimization problems. Further, it is compared with some well-known heuristic search methods. The obtained results indicate not only the optimization capability of the proposed framework, but also its ability to obtain accurate solutions and to achieve higher convergence precision.