Pareto Optimization with Robust Evaluation for Noisy Subset Selection

2025-01-12

AI Summary

This paper addresses the noisy subset selection problem under cardinality constraints, where objective function evaluations are highly noisy and computationally expensive. We propose the first method that integrates a robust evaluation function into a multi-objective Pareto optimization framework, simultaneously optimizing solution quality and subset size. Our approach jointly incorporates noise modeling, noise-resilient sampling, and evolutionary strategies to balance robustness and efficiency. Experiments on real-world influence maximization and sparse regression benchmarks demonstrate significant improvements over greedy algorithms, POSS, and PONSS. Ablation studies confirm that the robust evaluation module is the key driver of performance gains.

Technology Category

Search and Optimization: Sampling/Simulation-based SearchReasoning under Uncertainty: Stochastic OptimizationConstraint Satisfaction and Optimization: Constraint Optimization

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Subset selection is a fundamental problem in combinatorial optimization, which has a wide range of applications such as influence maximization and sparse regression. The goal is to select a subset of limited size from a ground set in order to maximize a given objective function. However, the evaluation of the objective function in real-world scenarios is often noisy. Previous algorithms, including the greedy algorithm and multi-objective evolutionary algorithms POSS and PONSS, either struggle in noisy environments or consume excessive computational resources. In this paper, we focus on the noisy subset selection problem with a cardinality constraint, where the evaluation of a subset is noisy. We propose a novel approach based on Pareto Optimization with Robust Evaluation for noisy subset selection (PORE), which maximizes a robust evaluation function and minimizes the subset size simultaneously. PORE can efficiently identify well-structured solutions and handle computational resources, addressing the limitations observed in PONSS. Our experiments, conducted on real-world datasets for influence maximization and sparse regression, demonstrate that PORE significantly outperforms previous methods, including the classical greedy algorithm, POSS, and PONSS. Further validation through ablation studies confirms the effectiveness of our robust evaluation function.

Problem

• Subset Selection
• Noise Mitigation
• Computational Efficiency

Innovation

• PORE method
• error-resilient selection
• superior performance