Abstract
Generally the knowledge of a collective, which is considered as a representative of the knowledge states in a collective, is often determined based on a single-stage approach. For big data, however, a collective is often very large, a multi-stage approach can be used. In this paper we present an improvement of the two-stage consensus-based approach for determining the knowledge of a large collective. For this aim, clustering methods are used to classify a large collective into smaller ones. The first stage of consensus choice aims at determining the representatives of these smaller collectives. Then these representatives will be treated as the knowledge states of a new collective which will be the subject for the second stage of consensus choice. In addition, all the collectives will be checked for susceptibility to consensus in both stages of consensus choice process. Through experiments analysis, the improvement method is useful in minimizing the difference between single-stage and two-stage consensus choice approaches in determining the knowledge of a large collective.
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Acknowledgement
We would like to thank Gideon Rosenblatt, Quoc Trung Bui, Erik Steiner for sharing datasets.
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Du Nguyen, V., Nguyen, N.T., Hwang, D. (2016). An Improvement of the Two-Stage Consensus-Based Approach for Determining the Knowledge of a Collective. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_10
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DOI: https://doi.org/10.1007/978-3-319-45243-2_10
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