Random set model for context-based classification
Jeremy Bolton, Paul Gader · 2008
In many scientific fields, data classification may be hindered by population correlated factors or hidden contexts. These factors greatly affect samplespsila 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 modelpsilas ability to consistently improve classification through context estimation.