| dc.description.abstract | Dynamic classifier selection with local accuracies (DCS-LA) uses k-Nearest Neighbor (kNN) to
combine multiple classifiers. The purpose of this research is to compare classification performance
using single classifiers (Rocchio, Bayes, and Bernoulli) with DCS-LA combining them. This research
classified 249 agricultural documents (3 classes) and 750 news documents (5 classes). The single
classifiers obtained different accuracies for the agricultural documents and the news documents. The
accuracies for agricultural documents are 52%, 44%, and 61.34% for Rocchio, Bayes, and Bernoulli,
respectively, while using news documents resulted in better accuracies, i.e., 81.6%, 86.4%, and 85.6%
for Rocchio, Bayes, and Bernoulli, respectively. Similarly, the accuracy of DCS-LA is 56% and 86%
for agricultural documents and news documents, respectively. This accuracy is higher than the
average accuracy of single classifiers because of the local accuracy calculation in DCS-LA. The
accuracy for the news documents is always higher than that of the agricultural documents, because the
classes in agricultural documents are highly related. The relation can be seen from the percentage of
common words shared among the classes. In the agriculture documents, in average of common words
is 48% while it is 10% in the news documents. | id |
