The problem addressed in this paper concerns the complexity reduction of the Nearest Feature Plane classifier, so that it may be applied also in dataset where the training set contains many patterns. This classifier considers, to classify a test pattern, the subspaces create by each combination of three training patterns. The main problem is that in dataset of high cardinality this method is unfeasible. A genetic algorithm is here used for dividing the training patterns in several clusters which centroids are used to build the feature planes used to classify the test set. The performance improvement with respect to other nearest neighbour based classifiers is validated through experiments with several benchmark datasets.
L. Nanni, A. Lumini (2009). Genetic nearest feature plane. EXPERT SYSTEMS WITH APPLICATIONS, 36, 838-843 [10.1016/j.eswa.2007.10.009].
Genetic nearest feature plane
NANNI, LORIS;LUMINI, ALESSANDRA
2009
Abstract
The problem addressed in this paper concerns the complexity reduction of the Nearest Feature Plane classifier, so that it may be applied also in dataset where the training set contains many patterns. This classifier considers, to classify a test pattern, the subspaces create by each combination of three training patterns. The main problem is that in dataset of high cardinality this method is unfeasible. A genetic algorithm is here used for dividing the training patterns in several clusters which centroids are used to build the feature planes used to classify the test set. The performance improvement with respect to other nearest neighbour based classifiers is validated through experiments with several benchmark datasets.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
