Robust Subset Selection by Greedy and Evolutionary Pareto Optimization

TL;DR

This paper introduces a robust subset selection approach that relaxes submodularity assumptions, proposing a greedy algorithm with approximation guarantees and an evolutionary Pareto optimization method, validated through theoretical analysis and experiments.

Contribution

It extends robust subset selection to non-submodular functions, providing approximation guarantees for both greedy and evolutionary algorithms.

Findings

Greedy algorithm achieves approximation ratio of 1-e^{-eta\gamma}.

EPORSS can find better subsets with more computation time.

Experimental results validate the effectiveness of both algorithms.

Abstract

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however, one often needs to find a subset which is robust against (i.e., is good over) a number of possible objective functions due to uncertainty, resulting in the problem of robust subset selection. This paper considers robust subset selection with monotone objective functions, relaxing the submodular property required by previous studies. We first show that the greedy algorithm can obtain an approximation ratio of $1-e^{-\beta\gamma}$, where $\beta$ and $\gamma$ are the correlation and submodularity ratios of the objective functions, respectively; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets. We prove that EPORSS can also be theoretically grounded, achieving a similar approximation guarantee to the greedy algorithm. In addition, we derive the lower bound of $\beta$ for the application of robust influence maximization, and further conduct experiments to validate the performance of the greedy algorithm and EPORSS.