Abstract
The consensus finding problem is known in the literature as a solution to inconsistency problems. Such inconsistency may come from different opinions of problem participants or data uncertainty. Consensus methods are used to find elements that represent all others in the inconsistent dataset and are a good compromise of the differing opinions. The O1 solution to consensus problem is best defined as finding the element that has the smallest sum of distances to all other elements. It is solved for many simple structures, but not for the complex tree structure. In this paper we propose several algorithms to find O1 consensus for complex trees (extended labeled trees), including a greedy algorithm and several approximate algorithms. We evaluate their approximation levels in terms of the 1-optimality criterion.
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Maleszka, M., Nguyen, N.T. (2012). Approximate Algorithms for Solving O1 Consensus Problems Using Complex Tree Structure. In: Nguyen, NT. (eds) Transactions on Computational Collective Intelligence VIII. Lecture Notes in Computer Science, vol 7430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34645-3_10
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DOI: https://doi.org/10.1007/978-3-642-34645-3_10
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