Understading Black Boxes: Knowledge Induction From Models

Due to regurations and laws prohibiting uses of private data on customers and their transactions in customer data base, most customer data sets are not easily accessable even in the same organizations. A solutio for this reguatory problems can be providing statistical summary of the data or models induced from the dat, instead of providing raw data sets. The models, however, have limited information on the original raw data set. This study explores possible solutions for these problems. The study uses prediction models from data on credit information of customers provided by a local bank in Seoul, S. Korea. This study suggests approaches in figuring what is inside of the non-rules based models such as regression models or neural network models. The study proposes several rule accumulation algorithms such as (RAA) and a GA-based rule refinement algorithm (GA-RRA) as possible solutions for the problems. The experiments show the performance of the random dataset, RAA, elimination of redundant rules (ERR), and GA-RRA

Science as an Anomaly-Driven Enterprise: A Computational Approach to Generating Acceptable Theory Revisions in the Face of Anomalous Data

Anomalous data lead to scientific discoveries. Although machine learning systems can be forced to resolve anomalous data, these systems use general learning algorithms to do so. To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts. Our experiments indicated that anomaly-driven approaches can lead to a richer set of model revisions than standard methods. Additionally we identified semantic and syntactic measures that are significantly correlated with the acceptability of model revisions. These results suggest that by interpreting data within the context of a model and by interpreting model revisions within the context of domain knowledge, discovery systems can more readily suggest accurate and acceptable anomaly resolutions