Early and Revocable Time Series Classification
  Many approaches have been proposed for early classification of time series in
light of itssignificance in a wide range of applications including healthcare,
transportation and fi-nance. Until now, the early classification problem has
been dealt with by considering onlyirrevocable decisions. This paper introduces
a new problem calledearly and revocabletimeseries classification, where the
decision maker can revoke its earlier decisions based on thenew available
measurements. In order to formalize and tackle this problem, we propose anew
cost-based framework and derive two new approaches from it. The first approach
doesnot consider explicitly the cost of changing decision, while the second one
does. Exten-sive experiments are conducted to evaluate these approaches on a
large benchmark of realdatasets. The empirical results obtained convincingly
show (i) that the ability of revok-ing decisions significantly improves
performance over the irrevocable regime, and (ii) thattaking into account the
cost of changing decision brings even better results in
general.Keywords:revocable decisions, cost estimation, online decision making
