  Joint feature selection and classification in an online setting is essential
for time-sensitive decision making. However, most existing methods treat this
coupled problem independently. Specifically, online feature selection methods
can handle either streaming features or data instances offline to produce a
fixed set of features for classification, while online classification methods
classify incoming instances using full knowledge about the feature space.
Nevertheless, all existing methods utilize a set of features, common for all
data instances, for classification. Instead, we propose a framework to perform
joint feature selection and classification on-the-fly, so as to minimize the
number of features evaluated for every data instance and maximize
classification accuracy. We derive the optimum solution of the associated
optimization problem and analyze its structure. Two algorithms are proposed,
ETANA and F-ETANA, which are based on the optimum solution and its properties.
We evaluate the performance of the proposed algorithms on several public
datasets, demonstrating (i) the dominance of the proposed algorithms over the
state-of-the-art, and (ii) its applicability to broad range of application
domains including clinical research and natural language processing.
