A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy
Küçük Resim Yok
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Suceava, Fac Electrical Eng
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.
Açıklama
Anahtar Kelimeler
Artificial Intelligence, Classification Algorithms, Decision Trees, Discernibility Function, Feature Selection
Kaynak
Advances In Electrical And Computer Engineering
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
13
Sayı
4
