A rule-based classifier with accurate and fast rule term induction for continuous attributes
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Rule-based classifiers are considered more expressive, human readable and less prone to over-fitting compared with decision trees, especially when there is noise in the data. Furthermore, rule-based classifiers do not suffer from the replicated subtree problem as classifiers induced by top down induction of decision trees (also known as 'Divide and Conquer'). This research explores some recent developments of a family of rulebased classifiers, the Prism family and more particular G-Prism-FB and G-Prism-DB algorithms, in terms of local discretisation methods used to induce rule terms for continuous data. The paper then proposes a new algorithm of the Prism family based on a combination of Gauss Probability Density Distribution (GPDD), InterQuartile Range (IQR) and data transformation methods. This new rule-based algorithm, termed G-Rules-IQR, is evaluated empirically and outperforms other members of the Prism family in execution time, accuracy and tentative accuracy.
|Title of host publication||Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018|
|Editors||M. Arif Wani, Moamar Sayed-Mouchaweh, Edwin Lughofer, Joao Gama, Mehmed Kantardzic|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 17 Jan 2019|
|Event||17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States|
Duration: 17 Dec 2018 → 20 Dec 2018
|Conference||17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018|
|Period||17/12/18 → 20/12/18|
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Accepted author manuscript (Post-print), 520 KB, PDF document