Analysis of Data Sets With Learning Conflicts for Machine Learning

In supervised learning, a machine learning system requires a data set. In occasions, however, the data set may have learning conflicts that may drastically affect the performance of the learning system. This paper presents a method to analyze the learning conflicts in a data set. Several computer simulations to test and validate our method are performed. Two common functions in the field of optimization are used to create clean data sets. The data sets are, then, contaminated with random data, and the total learning conflict level for each case is computed. The proposed algorithm is used to identify the learning conflicts that are intentionally inserted. Next, an artificial neural network is trained and evaluated using the contaminated data set. The algorithm proposed in this paper is used in a real-world application to detect problems in a data set for a refrigeration system. It is concluded that the algorithm can be used to improve the performance of machine learning systems.