A data-based method for harmonising heterogeneous data modelling techniques across data mining applications

MWITONDI, Kassim and SAID, Raed A. T. (2013). A data-based method for harmonising heterogeneous data modelling techniques across data mining applications. Journal of statistics applications and probability, 2 (3), 157-162.

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Abstract

We propose an iterative graphical data visualisation algorithm for optimal model selection. The algorithm is implemented on three domain-partitioning techniques - decision trees, neural networks and support vector machines. Each model is trained and tested on the Pima Indians and Bupa Liver Disorders datasets with the performance being assessed in a multi-step process. Firstly, the conventional ROC curves and the Youden Index are applied to determine the optimal model then sequential moving differences involving the fitted parameters - true and false positives – are extracted and their respective probability density estimations are used to track their variability using the proposed algorithm. The algorithm allows the use of data-dependent density bandwidths as tuning parameters in determining class separation across applications. Our results suggest that this novel approach yields robust predictions and minimizes data obscurity and over-fitting. The algorithm’s simple mechanics which derive from the standard confusion matrix and built-in graphical data visualisation and adaptive bandwidth features make it multidisciplinary compliant and easily comprehensible to non-specialists. The paper’s main outcomes are two-fold. Firstly, it combines the power of domain partitioning techniques on Bayesian foundations with graphical data visualisation to provide a dynamic, discernible and comprehensible information representation. Secondly, it demonstrates that by converting mathematical formulation into visual objects, multi-disciplinary teams can jointly enhance the knowledge of concepts and positively contribute towards global consistency in the data-based characterisation of various phenomena across disciplines.

Item Type:	Article
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Page Range:	157-162
Depositing User:	Helen Garner
Date Deposited:	13 Feb 2015 10:24
Last Modified:	18 Mar 2021 22:30
URI:	https://shura.shu.ac.uk/id/eprint/9390

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