Opening black box Data Mining models using Sensitivity Analysis

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 b...

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Bibliographic Details
Published in2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) pp. 341 - 348
Main Authors Cortez, P., Embrechts, M. J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Analytical models
Artificial neural networks
Delta modulation
Predictive models
Sensitivity
Support vector machines
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Summary: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.
ISBN:9781424499267
1424499267
DOI:10.1109/CIDM.2011.5949423