A Method for Training Data Selection based on LSTRf

Myunggwon Hwang, Yuna Jeong, Wonkyung Sung

http://doi.org/10.5626/JOK.2020.47.12.1192

This paper presents a data selection method that has a positive effect on learning for an efficient human-in-the-loop (HITL) process required for automated and intelligent artificial intelligence (AI) development. Our method first maps the training data onto a 2D distribution based on similarity, and then grids are laid out with a fixed ratio. By applying Least Slack Time Rate first (LSTRf) techniques, the data are selected based on the distribution consistency of the same class data within each grid. The finally selected data are used as convolutional neural network (CNN)-based classifiers to evaluate the performance. We carried out experiments on the CIFAR-10 dataset, and evaluated the effect of grid size and the number of data selected in one operation. The selected training data were compared to randomly selected data of the same size. The results verified that the smaller the grid size (0.008 and 0.005) and the greater the number selected in the single operation, the better the learning performance.