The Greedy Miser: Learning under Test-time Budgets
This addresses the need for efficient machine learning in industrial applications with test-time budget constraints, representing an incremental improvement over prior work.
The paper tackles the problem of controlling CPU-time during testing for machine learning algorithms by proposing the Greedy Miser algorithm, which incorporates feature extraction costs during training to minimize testing time, resulting in significant cost-effectiveness and scalability to larger datasets.
As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this paper, we propose an algorithm, the Greedy Miser, that incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing. The algorithm is a straightforward extension of stage-wise regression and is equally suitable for regression or multi-class classification. Compared to prior work, it is significantly more cost-effective and scales to larger data sets.