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|Title:||Fuzzy ART-based prototype classifier||Authors:||Dagher, Issam||Affiliations:||Department of Computer Engineering||Keywords:||Clusters
|Issue Date:||2011||Part of:||Computing journal||Volume:||92||Issue:||1||Start page:||49||End page:||63||Abstract:||
Prototype classifier is based on representing every cluster by a prototype. All the input patterns that belong to that cluster will have the same label as the prototype. It should be noted that a prototype does not have to be only one data. A cluster could be represented by more than one data. In this paper, the M-dimensional rectangle of the Fuzzy ART is used as a prototype. A new tree clustering structure replaces the training phase of Fuzzy ARTMAP. The obtained clusters are used to form the prototype rectangles. These rectangles will be used in the test phase of the Fuzzy ARTMAP. This algorithm is compared to the Nearest Neighbor classifier, the Fuzzy ARTMAP, C4.5, and the fuzzy ART-Var algorithms for different values of the vigilance parameter. Databases from the UCI repository will be used for comparison. Experimental results show the good generalization capability of this new algorithm.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/2026||Ezproxy URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
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