ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

Abstract

A reliable and precise tool for medical machine learning is in demand. The diagnosis datasets are mostly unbalanced. To propose an accurate prediction tool for medical data we need an accurate machine-learning algorithm for unbalanced data classification. In binary class unbalanced medical dataset, accurate prediction of the minority class is important. Traditional classifiers designed to improve accuracy by giving more weight to the majority class. Existing techniques gives good results by accurately classifying the majority class. Despite the fact that they misclassify the minority cases, the total accuracy value does not reflect this. When the misclassification cost of minority class is high, research should focus on reducing the total misclassification cost. This paper presents a new cost-sensitive classification algorithm that classifies unbalanced data accurately without compromising the accuracy of the minority class. Our proposed minority-sensitive decision tree algorithm employs new splitting criteria called MSplit to ensure accurate prediction of the minority class. The proposed splitting criteria MSplit derived from the exclusive causes of the minority class. For our experiment, we mainly focused on the breast cancer dataset by considering its importance in women's health. Our proposed model shows good results as compared to the recent studies of breast cancer detection. It shows 0.074 misclassification cost that is the least among the other comparison methods. Our model improves the performance for other unbalanced medical datasets as well.