Abstract

Abstract: Our research focuses on two goals. First, we seek to demonstrate that data quality is an important component of machine learning tools and that the quality of our data should be carefully considered when developing and using these tools. We believe that while the importance of data quality is now understood in the business community, where researchers have equated quality decision making to earnings, in the machine learning community this realization has not yet occurred. Therefore, we embark upon research into the effects of data quality upon these algorithms in an effort to demonstrate that data quality is a large factor in the outcomes of these algorithms and should be given more respect in their design. Second, after providing evidence that data quality is a large factor in these algorithms, we develop and test some preliminary methods which incorporate data quality assessments, thus creating more robust and useful algorithms. We believe these modifications and methods can have profound effects upon the usage of these machine learning algorithms in actual practice, particularly in the Law Enforcement community.

Citations