Appositeness of Hoeffding tree models for breast cancer classification
Keywords:
bagging, boosting, breast cancer (BC), class balancer (CB), decision tree (DT), ensemble, Hoeffding tree (HT)Abstract
Supervised machine learning models have been shown to be effective in disease-related classification and prediction tasks by employing several classifiers. A prominent category among the set of supervised machine learners is decision trees. Decision Trees comprises of an assortment of tree classifiers. Each of these types of decision trees are extensively used as supervised learners for various classification problems. In this paper, to deal with the classification of breast cancer tumours into malignant or benign types, a subcategory of decision trees so called Hoeffding Trees are employed. Hoeffding Trees is a type of decision tree classifier that are usually effective when working with data streams. In this paper, we explore the performance and appropriateness of Hoeffding trees in building models to classify breast cancer tumours as either benign or malignant. Individual and ensemble models using Hoeffding trees are implemented for classification of breast cancer. In the work proposed here a class-balancer Hoeffding Tree model is realized and it was seen demonstrating the best performance among the different Hoeffding Tree models employed. The proposed model yielded an accuracy of 97.9%. Several other performance measures are also used to evaluate the performance of the implemented Hoeffding tree models. This work highlights the appositeness of Hoeffding tree models for breast cancer classification.
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