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

arXiv: https://arxiv.org/abs/2306.01029 · HTML: https://mlsanity.com/paper/2306.01029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review](https://mlsanity.com/paper/1708.04321/) (2017)
- [Insights into Performance Fitness and Error Metrics for Machine Learning](https://mlsanity.com/paper/2006.00887/) (2020)
- [Nearest Neighbor-based Importance Weighting](https://mlsanity.com/paper/2102.02291/) (2021)
- [Learning similarity measures from data](https://mlsanity.com/paper/2001.05312/) (2020)
- [Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columns](https://mlsanity.com/paper/2108.09862/) (2021)
- [Dimensionality Reduction for Categorical Data](https://mlsanity.com/paper/2112.00362/) (2021)
- [Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study](https://mlsanity.com/paper/2003.01475/) (2020)

## Built on by (most cited first, 3 of 3)

- [Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications](https://mlsanity.com/paper/2405.13331/) (2024)
- [SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems](https://mlsanity.com/paper/2407.07222/) (2024)
- [SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration](https://mlsanity.com/paper/2411.03358/) (2024)

## Abstract (arXiv)

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.