Abstract
This chapter studies an extension of the subset selection problem, i.e., maximizing monotone k-submodular functions subject to a size constraint. Based on Pareto optimization, we present the POkSS algorithm for the problem, which is proven to have the state-of-the-art performance and is verified empirically on the applications of influence maximization, information coverage maximization, and sensor placement experiments.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Subset Selection: k-Submodular Maximization. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_15
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DOI: https://doi.org/10.1007/978-981-13-5956-9_15
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-13-5956-9
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