Generalized-Metric-Based Pattern Recognition Using Ordered Pair of Normalized Real Numbers

This paper proposes a generalized-metric-based pattern recognition method using Ordered Pairs of Normalized Real Numbers (OPNs). The proposed approach overcomes the limitations of real-valued metrics by effectively extracting high-dimensional information from feature interactions, which traditional distances cannot capture. Specifically, it employs a feature pairing that combines original features into real-number pairs to capture inherent feature correlations. A generalized metric built on OPNs theory allows effective comparison of distances expressed as real-number pairs. To address the combinatorial explosion problem in high-dimensional data, a correlation-based pre-screening strategy for pairing is introduced, significantly reducing the search space while maintaining classification performance. Experiments on multiple datasets show that this method achieves significantly higher accuracy than traditional distances and some deep learning models, reaching 95.33% on Iris and 90.00% on Seeds, with statistical tests confirming its effectiveness and stability.