A data-driven method for selecting optimal models based on graphical visualisation of differences in sequentially fitted ROC model parameters.

MWITONDI, Kassim, MOUSTAFA, Rida and HADI, Ali (2011). A data-driven method for selecting optimal models based on graphical visualisation of differences in sequentially fitted ROC model parameters. CODATA Data Science Journal. (In Press)

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Abstract

Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index; moving differences between sequential fitted parameters are then extracted and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance. Key Words: Bayesian Error, Data Mining, Decision Trees, Domain Partitioning, Data Visualisation, Optimal Bandwidth, ROC curves, Visual Analytics, Youden Index

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