Robust Hybrid Classification Methods and Applications

Abstract

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