A discretization method based on maximizing the area under receiver operating characteristic curve
Guvenir, H. A.
International Journal of Pattern Recognition and Artificial Intelligence
0218-0014 (Print)1793-6381 (Online)
World Scientific Publishing Company
1350002-1 - 1350002-26
MetadataShow full item record
Kurtcephe, M., & Güvenir, H. A. (2013). A discretization method based on maximizing the area under receiver operating characteristic curve. International Journal of Pattern Recognition and Artificial Intelligence, 27(01), 1350002.
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/13057
Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD uses the sorted order of the continuous values of a feature and discretizes the feature in such a way that the AUC based on that feature is to be maximized. The proposed method is compared with alternative discretization methods such as ChiMerge, Entropy-Minimum Description Length Principle (MDLP), Fixed Frequency Discretization (FFD), and Proportional Discretization (PD). FFD and PD have been recently proposed and are designed for Naive Bayes learning. ChiMerge is a merging discretization method as the MAD method. Evaluations are performed in terms of M-Measure, an AUC-based metric for multi-class classification, and accuracy values obtained from Naive Bayes and Aggregating One-Dependence Estimators (AODE) algorithms by using real-world datasets. Empirical results show that MAD is a strong candidate to be a good alternative to other discretization methods.