Abstract
It has been established that committee classifiers, in which the outputs of different, individual network classifiers are combined in various ways, can produce better accuracy than the best individual in the committee. We describe results showing that these advantages are obtained when neural networks are applied to a taxonomic problem in marine science: the classification of images of marine phytoplankton. Significant benefits were found when individual networks, trained on different classes of input, having comparable individual performances, were combined. Combining networks of very different accuracy did not improve performance when measured against the best single network, but nor was it reduced. An alternative architecture, which we term a collective machine, in which the different data types are combined in a single network, was found to have significantly better accuracy than the committee machine architectures. The performance gains and resilience to non-discriminatory types of data suggest the techniques have great utility in the development of general purpose, network classifiers.
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Ellis, R., Simpson, R., Culverhouse, P.F. et al. Committees, collectives and individuals: Expert visual classification by neural network. Neural Comput & Applic 5, 99–105 (1997). https://doi.org/10.1007/BF01501174
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DOI: https://doi.org/10.1007/BF01501174