SPINEX: Similarity-based Predictions and Explainable Neighbors
  Exploration for Regression and Classification Tasks in Machine Learning
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.