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Combining heterogeneous classifiers via granular prototypes. (2018)
Journal Article
NGUYEN, T.T., NGUYEN, M.P., PHAM, X.C., LIEW, A. W.-C. and PEDRYCZ, W. 2018. Combining heterogeneous classifiers via granular prototypes. Applied soft computing [online], 73, pages 795-815. Available from: https://doi.org/10.1016/j.asoc.2018.09.021

In this study, a novel framework to combine multiple classifiers in an ensemble system is introduced. Here we exploit the concept of information granule to construct granular prototypes for each class on the outputs of an ensemble of base classifiers... Read More about Combining heterogeneous classifiers via granular prototypes..

Aggregation of classifiers: a justifiable information granularity approach. (2018)
Journal Article
NGUYEN, T.T., PHAM, X.C., LIEW, A.W.-C. and PEDRYCZ, W. 2019. Aggregation of classifiers: a justifiable information granularity approach. IEEE transactions on cybernetics [online], 49(6), pages 2168-2177. Available from: https://doi.org/10.1109/TCYB.2018.2821679

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from th... Read More about Aggregation of classifiers: a justifiable information granularity approach..