Comparison of the proposed algorithms with classical classifiers.

<p>Comparison of the proposed algorithms with classical classifiers.</p>

Saved in:

Bibliographic Details
Main Author:	Kaveh Faraji Googerdchi (18778727) (author)
Other Authors:	Shahrokh Asadi (18778730) (author), Seyed Mohammadbagher Jafari (18778733) (author)
Published:	2024
Subjects:	Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified two essential principles mobile operator database diverse base classifiers data imbalance problem another objective function 89 %, showcasing customer churn modeling constructing ensemble classifiers key performance metrics propose two multi outperform existing models evaluate ensemble performance ensemble learning models customer churn prediction objective evolutionary clustering novel diversity measure churn prediction accuracy ensemble models churn prediction prediction performance novel multi proposed models telecommunication using second model practical application organizational decision moeecs ), mitigate costs foster growth first model findings underscore dataset collected considerable challenge commonly used 2 attains
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items