In many scientific fields, data classification may be hindered by population correlated factors or hidden contexts. These factors greatly affect samples' values making it difficult for standard classification models to perform well on a consistent basis. A general random set model is presented for context-based classification. An implementation is provided based on Possibility Theory. The result is a robust classifier that can intrinsically identify hidden contexts and classify data accordingly. The random set model is compared to standard kNN and set-based kNN. Results from synthetic data illustrate the random set model's ability to consistently improve classification through context estimation.
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