Abstract
This chapter studies minimizing the ratio \( f/g \), such as optimizing the F-measure objective in machine learning tasks. We prove the approximation bound of a greedy-style algorithm, as well as a Pareto optimization based algorithm PORM, where PORM is shown to be able to achieve better performance. The advantage of PORM is also verified by empirical results on an application of F-measure maximization.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Subset Selection: Ratio Minimization. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_16
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DOI: https://doi.org/10.1007/978-981-13-5956-9_16
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