Classification Under Human Assistance
This work addresses the challenge of integrating human expertise into machine learning for improved accuracy in specific instances, such as medical diagnosis, though it is incremental in its approach.
The paper tackles the problem of designing classifiers optimized for varying levels of human assistance, showing that the problem is NP-hard but can be approximated using greedy algorithms for support vector machines. Experiments on medical diagnosis data demonstrate that these models outperform fully automated ones and humans alone.
Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by this empirical observation, our goal is to design classifiers that are optimized to operate under different automation levels. More specifically, we focus on convex margin-based classifiers and first show that the problem is NP-hard. Then, we further show that, for support vector machines, the corresponding objective function can be expressed as the difference of two functions f = g - c, where g is monotone, non-negative and γ-weakly submodular, and c is non-negative and modular. This representation allows a recently introduced deterministic greedy algorithm, as well as a more efficient randomized variant of the algorithm, to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.