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.

翻译：机器学习领域近年来取得了显著进展。然而，许多现有算法缺乏可解释性，且在高维数据和类别不平衡数据上表现不佳。本文提出SPINEX，一种新颖的基于相似性的可解释邻域探索算法，旨在解决上述局限性。该算法通过集成学习与特征交互分析，量化每个特征对预测的贡献并识别特征间的交互作用，从而实现精准预测与有意义洞察，增强算法的可解释性。为评估SPINEX性能，我们在59个合成数据集与真实数据集上对回归和分类任务开展了大量实验。结果表明，SPINEX取得了与常用机器学习算法相当的性能，且在部分场景中可能更优。研究结果同样验证了SPINEX的有效性与竞争力，使其成为各类实际应用中的一种有前景的解决方案。