Abstract
This paper is devoted to data decomposition analysis. We decompose data into functional groups depending on their complexity in terms of classification. We use consensus of classifiers as an effective algorithm for data decomposition. Present research considers data decomposition into two subsets of “easy” and “difficult” or “ambiguous” data. The easiest part of data is classified during decomposition using consensus of classifiers. For other part of data one has to apply other classifiers or classifier combination. One can prove experimentally that afore mentioned data decomposition using optimal consensus of classifiers leads to better performance and generalization ability of the entire classification algorithm.
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Tayanov, V., Krzyżak, A., Suen, C. (2017). Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_45
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DOI: https://doi.org/10.1007/978-3-319-59876-5_45
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