Open AccessUMAP Based Data Validity Evaluation for Artificial Intelligence Systems
Author(s) -
Han Seong Son,
Hyemin Lim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012003
Subject(s) - computer science , data validation , artificial intelligence , machine learning , data collection , projection (relational algebra) , data mining , mathematics , algorithm , statistics , database
Although comparison is one of the most useful data validity evaluation method, it has a few potential drawbacks. One of them is that results from the training or the testing a machine learning model shall be obtained. Another one is that uncertainty due to the machine learning model itself may cause a difficulty in evaluating the data validity. In this paper, a new data validity evaluation method was proposed so that these drawbacks can be made up by the proposed method. The proposed method enables a data validity evaluation to be performed in data collection phase or data pre-processing phase. Uniform Manifold Approximation and Projection (UMAP) offers the methodological background to the proposed method.