TY  - JOUR
T1  - Autoencoder-based Learning Contribution Measurement Method for Training Data Selection
AU  - Jeong, Yuna 
AU  - Hwang, Myunggwon 
AU  - Sung, Wonkyung 
JO  - Journal of KIISE, JOK
PY  - 2021
DA  - 2021/1/14
DO  - 10.5626/JOK.2021.48.2.195
KW  - latent space
KW  - data selection
KW  - training data
KW  - autoencoder
KW  - machine learning
AB  - Despite recent significant performance improvements, the iterative process of machine-learning algorithms makes development and utilization difficult and time-consuming. In this paper, we present a data-selection method that reduces the time required by providing an approximate solution . First, data are mapped to a feature vector in latent space based on an Autoencoder, with high weight given to data with high learning contribution that are relatively difficult to learn. Finally, data are ranked and selected based on weight and used for training. Experimental results showed that the proposed method selected data that achieve higher performance than random sampling.