Opening black box Data Mining models using Sensitivity Analysis

• Published in: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) pp. 341 - 348
• Main Authors: Cortez, P., Embrechts, M. J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2011
• Subjects: Analytical models; Artificial neural networks; Delta modulation; Predictive models; Sensitivity; Support vector machines
• DOI: 10.1109/CIDM.2011.5949423
• ISBN: 9781424499267; 1424499267

There are several supervised learning Data Mining (DM) methods, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, that often attain high quality predictions, although the obtained models are difficult to interpret by humans. In this paper, we open these black box DM models by using a novel visualization approach that is based on a Sensitivity Analysis (SA) method. In particular, we propose a Global SA (GSA), which extends the applicability of previous SA methods (e.g. to classification tasks), and several visualization techniques (e.g. variable effect characteristic curve), for assessing input relevance and effects on the model's responses. We show the GSA capabilities by conducting several experiments, using a NN ensemble and SVM model, in both synthetic and real-world datasets.