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Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection - 2019

Incremental Learning Of Concept Drift In Multiple Instance Learning For Industrial Visual Inspection

Research Area:  Machine Learning


Most Multiple Instance Learning (MIL) algorithms are designed with the assumption that the target concept is stationary in time, i.e. it is drawn from a stationary unknown distribution. However, in real industrial applications, like automatic visual inspection where defects may evolve, MIL has to deal with changing target concepts whose statistical characteristics may vary over time. Despite this fact, there is little discussion about how to learn from data in non-stationary environments (or data with concept drift) using multiple instance learners. In this work, an incremental MIL algorithm is proposed in order to learn non-stationary and recurrent target concepts in industrial visual inspection applications. Experiments on both synthetic and real-world datasets are conducted to test the performance of the proposed approach. Real-world datasets come from the automatic visual inspection task in industry. The experimental results show that the proposed approach is able to handle changing target concepts over time.


Author(s) Name:  Carlos Mera, Mauricio Orozco-Alzate, John Branch

Journal name:  Computers in Industry

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.compind.2019.04.006

Volume Information:  Volume 109, August 2019, Pages 153-164