A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches

G Prasad; DA Bell; Martin McGinnity; QX Wu

doi:10.1109/TKDE.2007.250582

A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches

G Prasad, DA Bell, Martin McGinnity, QX Wu

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	17-28
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	19
Issue number	1
DOIs	https://doi.org/10.1109/TKDE.2007.250582
Publication status	Published (in print/issue) - 1 Jan 2007

Bibliographical note

Other Details
------------------------------------
This paper investigated issues associated with machine learning approaches for enhanced decision-making performance with real-world data-sets that have mixed data types, incomplete instances, and variable feature space. With symbolic machine learning approaches, there is a requirement to transform continuous attribute values to symbolic data. The paper develops a novel distribution-index-based discretiser for such a transformation. The discretiser is combined with a rough-set theory based approach to further improve decision accuracy. Experimental results on benchmark data sets demonstrate that the approach provides superior accuracies for the same data sets compared with existing techniques. The work contributed to a successful PhD completion.

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10.1109/TKDE.2007.250582

Cite this

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title = "A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches",

author = "G Prasad and DA Bell and Martin McGinnity and QX Wu",

note = "Other Details ------------------------------------ This paper investigated issues associated with machine learning approaches for enhanced decision-making performance with real-world data-sets that have mixed data types, incomplete instances, and variable feature space. With symbolic machine learning approaches, there is a requirement to transform continuous attribute values to symbolic data. The paper develops a novel distribution-index-based discretiser for such a transformation. The discretiser is combined with a rough-set theory based approach to further improve decision accuracy. Experimental results on benchmark data sets demonstrate that the approach provides superior accuracies for the same data sets compared with existing techniques. The work contributed to a successful PhD completion.",

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