The U-curve optimization problem: improvements on the original algorithm and time complexity analysis
This addresses feature selection in machine learning, but appears incremental as it improves upon an existing algorithm.
The authors tackled the U-curve optimization problem, showing that the existing U-Curve algorithm is suboptimal and introducing the U-Curve-Search (UCS) algorithm, which outperformed competitors like UBB and SFFS in experiments.
The U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice. This problem can be applied to model the classical feature selection problem in Machine Learning. Recently, the U-Curve algorithm was proposed to give optimal solutions to the U-curve problem. In this article, we point out that the U-Curve algorithm is in fact suboptimal, and introduce the U-Curve-Search (UCS) algorithm, which is actually optimal. We also present the results of optimal and suboptimal experiments, in which UCS is compared with the UBB optimal branch-and-bound algorithm and the SFFS heuristic, respectively. We show that, in both experiments, $\proc{UCS}$ had a better performance than its competitor. Finally, we analyze the obtained results and point out improvements on UCS that might enhance the performance of this algorithm.