Abstract
This paper illustrates a data mining application using two-dimensional (2-D) neural network sensitivity analysis for gaining insight into data strip mining problems. Data strip mining refers to predictive data mining problems where there are a large number of descriptive features, and the number of features is on the order of or exceeds the number of data records (e.g., 100 to 1000 features for 50 to 300 data records). After reducing the number of descriptive features to a manageable set using 1-D neural network sensitivity analysis (e.g., 40 features), a 2-D neural network sensitivity analysis allows the user to visualize variations in the response to identify relevant combinations of features. Each relevant combination can then be analyzed independently to look for interesting patterns and relationships, and can be used in this way to either prune more features or to get insight into the underlying rules for the model. 2-D sensitivity analysis enables the exploration of relevant relationships and features resulting in more robust, meaningful, and efficient models. This methodology was applied to an in-silico drug design problem with 64 molecules and 160 d escriptive features.