‬‬‬Mohammad Khaled al-Bashiti

M. Z. Naser, M. K. albashiti, A. Z. Naser

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algorithm combines ensemble learning and feature interaction analysis to achieve accurate predictions and meaningful insights by quantifying each feature's contribution to predictions and identifying interactions between features, thereby enhancing the interpretability of the algorithm. To evaluate the performance of SPINEX, extensive experiments on 59 synthetic and real datasets were conducted for both regression and classification tasks. The results demonstrate that SPINEX achieves comparative performance and, in some scenarios, may outperform commonly adopted ML algorithms. The same findings demonstrate the effectiveness and competitiveness of SPINEX, making it a promising approach for various real-world applications.

M. Z. Naser, Mohammad Khaled al-Bashiti, Arash Teymori Gharah Tapeh et al.

In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions. While several functions have been developed and derived over the past decades, little information is available on the mathematical and visual description, range of suitability, and applications of many such functions. To bridge this knowledge gap, this review provides an exhaustive survey of more than 300 benchmark functions used in the evaluation of optimization and metaheuristics algorithms. This review first catalogs benchmark and test functions based on their characteristics, complexity, properties, visuals, and domain implications to offer a wide view that aids in selecting appropriate benchmarks for various algorithmic challenges. This review also lists the 25 most commonly used functions in the open literature and proposes two new, highly dimensional, dynamic and challenging functions that could be used for testing new algorithms. Finally, this review identifies gaps in current benchmarking practices and suggests directions for future research.