[1N1] [General Session] 2. Machine Learning

[1N1-02] The Method of Machine Learning Considering Tamper of Training Data

Tenshi Yanagimachi¹, 〇Yoshiteru Ishida¹ (1. Toyohashi University of Technology)

Keywords:AI, machine learning, tampered data, label noise

Big data is increasingly used as training data for machine learning. However, large-scale data such as big data is not always appropriate as training data at all times. Particularly when collecting data from Web services such as SNS, unspecified number of people using the service can indirectly tamper with data. In this research, we propose a learning method and verify its effectiveness so as to obtain a learning result close to the case without tampering even in an environment in which part of the training data has been tampered with. In this method, learning is divided into two stages, and the reliability of the training data is estimated using the first stage learning result, thereby assisting the exclusion of tamperd data by human beings.