Pruning Classification Rules with Reference Vector Selection Methods

Attempts to extract logical rules from data often lead to large sets of classification rules that need to be pruned. Training two classifiers, the C4.5 decision tree and the Non-Nested Generalized Exemplars (NNGE) covering algorithm, on datasets that have been reduced earlier with the EkP instance compressor leads to statistically significantly lower number of derived rules with nonsignificant degradation of results. Similar results have been observed with other popular instance filters used for data pruning. Numerical experiments presented here illustrate that it is possible to extract more interesting and simpler sets of rules from filtered datasets. This enables a better understanding of knowledge structures when data is explored using algorithms that tend to induce a large number of classification rules.

• Type: Conference
• Year: 2010
• Where: ICAISC
• Authors: Karol Grudzinski, Marek Grochowski, Wlodzislaw Duch
• Keywords: Artificial Intelligence, Classification Rules, EkP Instance Compressor, ICAISC 2010, Non-Nested Generalized Exemplars