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kNNPC (k-Nearest Neighbor Algorithm Per-Class): Classification Method Predicting Extrapolation Regions With Reasonable Probability
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DOI：10.1002/cem.70135.png)
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Abstract
En 中文
In classification tasks, predicted class probabilities are often overconfident for samples outside the training distribution, which can lead to unsafe decision-making. kNNPC, a k-nearest neighbor algorithm per class, is proposed to mitigate such overconfidence under extrapolation or distribution shift. For a given test sample, kNNPC adjusts the predicted class-probability vector so that it becomes closer to a maximum-uncertainty distribution when the sample is dissimilar to the training data in the feature space. Importantly, kNNPC is not intended to identify extrapolation regions explicitly; rather, it provides a conservative probability output that serves as a practical safeguard against overconfident predictions. kNNPC was evaluated on benchmark and real-world datasets, including the Iris dataset, a toxicity classification dataset, and a superconducting critical-temperature dataset, and compares with standard probabilistic classifiers. The results indicate that kNNPC can effectively suppress overconfident probabilities for out-of-domain samples while maintaining comparable predictive performance. The Python code for the proposed method is available at https://github.com/hkaneko1985/dcekit.
Keywords:
classification
extrapolation regions
k-nearest neighbor algorithm
material design
molecular design
probability