Study on the Application of Rough Sets Theory in Machine Learning

As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". Machine learning has a wide spectrum of applications and has been paid many attentions by researchers. However, the quantitative measurement problem of the learning quality and the completeness of the supervisor's knowledge under incomplete information haven't been solved very well. Rough sets theory is a mathematical tool for extracting knowledge from uncertain and incomplete information. The paper firstly introduced some related concepts about machine learning and supervised learning, then, from the perspective of rough sets theory, studied the learning quality of machine learning and the completeness of supervisor's knowledge, which may provide some new ideas for studying the machine learning.