Using multiple classifier system for diagnosis of  endometriosis: an ensemble subspace approach

Akhoond, Mohammad Reza; Bagheri, Mohammad Ali; Mousavi, Ali; Moini, Ashraf

Using multiple classifier system for diagnosis of endometriosis: an ensemble subspace approach

Authors

¹ Department of Statistics, Shahid Chamran University, Ahvaz, Iran

² Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

³ 2Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

⁴ Department of Endocrinology and Female Infertility, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran

Abstract

One efficient approach in classification is using a set of individual classifiers and then combining their outputs, usually knows as ensemble classification or multiple classifier system. In this paper, an ensemble classification system based on the random subspace approach is employed for diagnosis of endometriosis, in which individual classifiers of the ensemble system are trained with different feature subsets. Finally, for classifying an unknown test sample, classifiers’ outputs are fused using the majority voting combination rule.

Keywords