Comparison of Classification Techniques on Energy Efficiency Dataset
Küçük Resim Yok
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The definition of the data mining can be told as to extract information or knowledge from large volumes of data. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet. This study make use of WEKA (Waikato Environment for Knowledge Analysis) to compare the different classification techniques on energy efficiency datasets. In this study 10 different Data Mining methods namely Bagging, Decorate, Rotation Forest, J48, NNge, K-Star, Naïve Bayes, Dagging, Bayes Net and JRip classification methods were applied on energy efficiency dataset that were taken from UCI Machine Learning Repository. When comparing the performances of algorithms it’s been found that Rotation Forest has highest accuracy whereas Dagging had the worst accuracy
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yapay Zeka
Kaynak
International Journal of Intelligent Systems and Applications in Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
5
Sayı
2
