1. A computer-implemented method of optimizing a classification algorithm, comprising: receiving, at a processor, a set of trained weighted feature functions for the classification algorithm, and a plurality of training instances; calculating an evaluation sequence for applying the trained weighted feature functions to the plurality of training instances that orders the feature functions in accordance with a measure of influence on the classification algorithm; determining at least one classification threshold for each step of the evaluation sequence, wherein each classification threshold indicates whether a classification decision can be made at an associated step of the evaluation sequence and the classification algorithm terminated without evaluating further feature functions; storing the evaluation sequence and classification thresholds at a storage device; and at least one of: A) the classification algorithm being a binary classifier in which the classification decision is one of: positive or negative, and the step of determining comprising: determining a first and second threshold for each step of the evaluation sequence; computing the first threshold using a maximum cumulative value at that step of the evaluation sequence from those training instances having a negative classification; and computing the second threshold using a minimum cumulative value at that step of the evaluation sequence from those training instances having a positive classification; or B) the step of calculating the evaluation sequence comprising: i) selecting a current evaluation sequence order; ii) determining an expected evaluation cost for the current evaluation sequence; iii) randomly selecting at least one neighboring evaluation sequence order; iv) determining an expected evaluation cost for the or each neighboring evaluation sequence order; v) determining whether the minimum expected evaluation cost for the or each neighboring evaluation sequence order is less than the expected evaluation cost for the current evaluation sequence, and, if so, updating the current evaluation sequence order to the order having the minimum expected evaluation cost.