ENBIS-12 in Ljubljana

9 – 13 September 2012 Abstract submission: 15 January – 10 May 2012

Balancing Interpretation and Prediction Accuracy in Classification and Regression using Local Correlation Information

10 September 2012, 12:10 – 12:30


Submitted by
Marco P. Seabra dos Reis
Marco S. Reis (University of Coimbra)
Current methodologies for conducting classification and regression activities are strongly centred in optimizing estimation accuracy metrics, leaving to a secondary concern the interpretation of the results produced. However, there are an increasing number of applications where interpretation plays a central role in the analysis, and constitutes a major outcome (e.g., the inference of relevant associations in the analysis of complex systems, in order to understand its operation). In this communication, we present an integrated framework for regression and classification with interpretational-oriented features built-in. Such features are a result of inducing local associations between variables and using the resulting network structure to form, in a robust way, modules of associated variables. The network features will constrain the predictive space, in order to introduce interpretable elements into the final model. We have find out that the introduction of these constraints do not usually compromise the methods’ performance and, in fact, quite often leads to better predictive results, meaning that it is indeed possible to improve current methods at the interpretation and accuracy levels.

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