Strategic Classification with Non-Linear Classifiers

arXiv — cs.LG•Wednesday, November 5, 2025 at 5:00:00 AM

Recent research in strategic classification has expanded its scope by incorporating non-linear classifiers, moving beyond the traditional reliance on linear models. This shift aims to better capture the complexities of user behavior, particularly how individuals may strategically manipulate features in response to classification systems. By focusing on non-linear approaches, the study broadens the understanding of how classifiers interact with adaptive user strategies. Such advancements highlight the dynamic interplay between classification algorithms and user responses, emphasizing the importance of considering non-linear decision boundaries. This development is part of ongoing efforts to refine machine learning models to be more robust against strategic manipulation. The research contributes to the broader field of artificial intelligence by addressing challenges in predictive accuracy and fairness when users alter their behavior. Overall, the integration of non-linear classifiers marks a significant step in enhancing strategic classification frameworks.

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