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Ensembling Classifiers: An Application to Food Type Classification of Genus Conus

Rizavel C. Addawe, Zenaida G. Baoanan

Abstract


In this paper, the ensemble learning algorithms such as Bagging and Boosting were investigated. The task is to investigate possible improvement of the Meta learning techniques with base classifiers such as Random Forests, JRip, J48, and Logistic through boosting and bagging. The data sets used in the experiments are from genus Conus, a collection of morphometric parameters of 867 shells and their food types. The classifiers are designed to classify Conus according to their food types using eight morphometric parameters to address the problem of classification since the data contain rare events. This paper compared and discussed improvements in the individual classifier with their ensemble counterparts. WEKA, an interesting tool for machine learning, has been used for this investigation.

Keywords


WEKA, classifiers, bagging, boosting, logistic regression, random forest[RF].

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