Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications-Reference-Cited by-同舟云学术

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Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Published:2022 Issue:2 Volume:72 Page:3985-3997
ISSN:1546-2226
Container-title:Computers, Materials & Continua
language:en
Short-container-title:

Author:

Hamed M. Aly Rabab,I. Hussein Aziza,H. Rahouma Kamel

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modeling and Simulation,Biomaterials

Reference30 articles.

1. Using ML in Burnout Prediction: A survey;Grządzielewska;Child and Adolescent Social Work Journal,2021

2. Workforce optimization for bank operation centers: A machine learning approach;Serengil;Ijimai,2017

3. Employee turnover: A novel prediction solution with effective feature selection;Chang,2009

4. MLFramework for multi-level classification of company revenue;Choi;IEEE Access,2021

5. Chaotic variants of grasshopper optimization algorithm and their application to protein structure prediction;Saxena;Springer Tracts in Nature-Inspired Computing. Springer,2020

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