Conformal isotonic separation for binary classification

Abstract

Isotonic Separation (IS) is a non-parametric classification algorithm which constructs an isotonic function as a model from given data with monotonic nature. This paper proposes an approach in which a novel conformal prediction framework is deployed in two setups to improve the classification performance of isotonic separation: Transductive and Inductive method. This paper introduces a new Non-Conformity Measure (NCM) which is specific to isotonic separation to compute the uniqueness of a data point to a set of data points. This measure is deployed on two different forms of conformal prediction methods: Transductive Conformal Isotonic Separation (TCIS) and Inductive Conformal Isotonic Separation (ICIS). Experiments are conducted on different monotonic data sets using transductive and inductive isotonic separation techniques. Experimental and statistical results reveal that transductive and inductive conformal isotonic separation give significant results in terms of performance measures.