  In many real tasks the features are evolving, with some features being
vanished and some other features augmented. For example, in environment
monitoring some sensors expired whereas some new ones deployed; in mobile game
recommendation some games dropped whereas some new ones added. Learning with
such incremental and decremental features is crucial but rarely studied,
particularly when the data coming like a stream and thus it is infeasible to
keep the whole data for optimization. In this paper, we study this challenging
problem and present the OPID approach. Our approach attempts to compress
important information of vanished features into functions of survived features,
and then expand to include the augmented features. It is the one-pass learning
approach, which only needs to scan each instance once and does not need to
store the whole data, and thus satisfy the evolving streaming data nature. The
effectiveness of our approach is validated theoretically and empirically.
