IPBoost -- Non-Convex Boosting via Integer Programming
This addresses classification problems for machine learning practitioners, but appears incremental as it builds on existing boosting methods with a non-convex twist.
The paper tackles the problem of non-convex boosting in classification by using integer programming, demonstrating real-world practicability and circumventing shortcomings of convex approaches, with results reported as comparable to or better than state-of-the-art.
Recently non-convex optimization approaches for solving machine learning problems have gained significant attention. In this paper we explore non-convex boosting in classification by means of integer programming and demonstrate real-world practicability of the approach while circumventing shortcomings of convex boosting approaches. We report results that are comparable to or better than the current state-of-the-art.