Accuracy-Rejection Curves (ARCs) for Comparing Classification Methods with a Reject Option

Data extracted from microarrays are now considered an important source of knowledge about various diseases. Several studies based on microarray data and the use of receiver operating characteristics (ROC) graphs have compared supervised machine learning approaches. These comparisons are based on classification schemes in which all samples are classified, regardless of the degree of confidence associated with the classification of a particular sample on the basis of a given classifier. In the domain of healthcare, it is safer to refrain from classifying a sample if the confidence assigned to the classification is not high enough, rather than classifying all samples even if confidence is low. We describe an approach in which the performance of different classifiers is compared, with the possibility of rejection, based on several reject areas. Using a tradeoff between accuracy and rejection, we propose the use of accuracy-rejection curves (ARCs) and three types of relationship between AR...

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Malik Sajjad Ahmed Nadeem, Jean-Daniel Zucker, Blaise Hanczar
• Keywords: Binary Classification Problems, JMLR 2010, Microarray Data, Receiver Operating Characteristics