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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.08956 |
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| _version_ | 1866908703036276736 |
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| author | Pathak, Kumarjit K, Karthik Madan, Sachin Kapila, Jitin |
| author_facet | Pathak, Kumarjit K, Karthik Madan, Sachin Kapila, Jitin |
| contents | K-Nearest Neighbors (KNN) is one of the most used ML classifiers. However, if we observe closely, standard distance-weighted KNN and relative variants assume all 'k' neighbors are equally reliable. In heterogeneous feature space, this becomes a limitation that hinders reliability in predicting true levels of the observation.
We propose DW-KNN (Double Weighted KNN), a transparent and robust variant that integrates exponential distance with neighbor validity. This enables instance-level interpretability, suppresses noisy or mislabeled samples, and reduces hyperparameter sensitivity.
Comprehensive evaluation on 9 data-sets helps to demonstrate that DW-KNN achieves 0.8988 accuracy on average. It ranks 2nd among six methods and within 0.2% of the best-performing Ensemble KNN. It also exhibits the lowest cross-validation variance (0.0156), indicating reliable prediction stability. Statistical significance test confirmed ($p < 0.001$) improvement over compactness weighted KNN (+4.09\%) and Kernel weighted KNN (+1.13\%). The method provides a simple yet effective alternative to complex adaptive schemes, particularly valuable for high-stakes applications requiring explainable predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DW-KNN: A Transparent Local Classifier Integrating Distance Consistency and Neighbor Reliability Pathak, Kumarjit K, Karthik Madan, Sachin Kapila, Jitin Machine Learning K-Nearest Neighbors (KNN) is one of the most used ML classifiers. However, if we observe closely, standard distance-weighted KNN and relative variants assume all 'k' neighbors are equally reliable. In heterogeneous feature space, this becomes a limitation that hinders reliability in predicting true levels of the observation. We propose DW-KNN (Double Weighted KNN), a transparent and robust variant that integrates exponential distance with neighbor validity. This enables instance-level interpretability, suppresses noisy or mislabeled samples, and reduces hyperparameter sensitivity. Comprehensive evaluation on 9 data-sets helps to demonstrate that DW-KNN achieves 0.8988 accuracy on average. It ranks 2nd among six methods and within 0.2% of the best-performing Ensemble KNN. It also exhibits the lowest cross-validation variance (0.0156), indicating reliable prediction stability. Statistical significance test confirmed ($p < 0.001$) improvement over compactness weighted KNN (+4.09\%) and Kernel weighted KNN (+1.13\%). The method provides a simple yet effective alternative to complex adaptive schemes, particularly valuable for high-stakes applications requiring explainable predictions. |
| title | DW-KNN: A Transparent Local Classifier Integrating Distance Consistency and Neighbor Reliability |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.08956 |