SEQUENTIAL DATUM–WISE JOINT FEATURE SELECTION AND CLASSIFICATION IN THE PRESENCE OF EXTERNAL CLASSIFIER

We introduce a supervised machine learning framework for sequential datum–wise joint feature selection and classification. Our proposed approach sequentially acquires features one at a time during testing until it decides that acquiring more features will not improve label assignment. At that point, and in contrast to prior art, it assigns a label to the example under consideration by selecting between a simple internal and a more powerful external classifier. Easy–to–classify examples are handled by the internal classifier, which assigns labels based on the lowest expected misclassification cost. On the other hand, difficult–to–classify examples are forwarded to the external classifier to be assigned a label based on the acquired features. We demonstrate the performance of the proposed approach compared to existing methods using six publicly available datasets. Experiments indicate that the proposed approach improves accuracy up to 50% with respect to existing sequential methods, while acquiring up to 85% less number of features on average.