Robust Hybrid Classification Methods and Applications

نویسندگان

چکیده

The sample mean classifier, such as the nearest classifier (NMC) and Bayes is not robust due to influence of outliers. Enhancing performance these methods may result in vital information loss weighting or data deletion. focus this study develop hybrid univariate classifiers that do rely on following transformation methods, least square approach (LSA) linear prediction (LPA), are applied estimate parameters interest achieve objectives study. LSA LPA estimates two groups classifiers. We further predicted from four These investigate whether cattle horn base width length could be used determine gender. also classification shapes classify banana variety. NMC, LSA, LPA, showed gender determined using measurement. analysis revealed comparative results sets demonstrated all have over 90% accuracy. findings affirmed satisfy data-dependent theory suitable for classifying agricultural products. Therefore, proposed perform tasks efficiently many fields

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ژورنال

عنوان ژورنال: pertanika journal of science and technology

سال: 2022

ISSN: ['0128-7680', '2231-8526']

DOI: https://doi.org/10.47836/pjst.30.4.29