An Ensemble-based Active Learning for Breast Cancer Classification

Abstract

Ensembled machine learning paradigms enable base learners to provide more accurate predictions than a standard approach using a single learner. Though the ensemble learning decreases variance or bias, improving predictions, limited literatures have been reported with an active learning strategy narrowing uncertainty in prediction. We present an ensemble based active learning approach for breast cancer detection, averaging predictions from the start of the art machine learning models on histopathology images. We demonstrate that the ensemble based active learning approach outperforms other approaches on breast cancer detection.