"Learning Comprehensible Predictive Models from Data"

4/2/1999: [time not recorded]
[location not recorded]

Abstract: Knowledge discovery in databases is a field whose goal is to turn data into knowledge. For example, by analyzing a database of credit card customers we can determine what types of customers are most likely to be profitable for the company. By "mining" databases of medical records, new cost-effective procedures for screening for diseases may be uncovered. Several decades of research in statistics, neural networks and artificial intelligence have identified a variety of approaches that produce accurate descriptive or predictive models. However, experts are unwilling to accept the results of these techniques when they don't make sense, are difficult to understand, or violate prior understanding. Here, we discuss factors that make learned knowledge acceptable to experts and discuss modifications to rule learning, linear regression and text classification algorithms that make the learned models more comprehensible.

Last updated: Mon Jun 19 17:44:06 2006