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A comparative evaluation approach for the classification of rotifers with modified non-parametric kNN by Chan-Yun Yang; Jui-Jen Chou is a Computer Science article available to read on EtoBox.

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In this study-aimed to achieve optimal accuracy in the classification of rotifers according to the number of eggs carried-several modifications to the basic kNN method have been proposed and assessed. Six distinct kNN rules as well as several additional hybrid models were, in fact, devised or employed and their precision compared. Meanwhile, the data sets used in the evaluation of each of these methods were acquired from rotifer images generated via the shape moments approach. Both the original data sets and the edited ones, formed by removing outliers from the originals, were used in the evaluation of these adjusted models. Through a process of comparative evaluation, several of the modified algorithms proposed-comprising both individual and hybrid models-were found to perform better overall than the classical kNN method. Refinements related to class-size weighting, in particular, were shown to heighten the accuracy of the classical kNN model considerably. Close evaluation of the various models created revealed kNN-CCS and F-kNN-CCS, in their application to the edited data sets, to be the most reliable individual modified and hybrid models respectively, with levels of accuracy gre

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Author

Chan-Yun Yang; Jui-Jen Chou

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0262-8856)

Published

2005

Language

EN

Field

Computer Science (Physical Sciences)