Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications
Source abstract
Abstract
This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees’ performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by “KF” with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (30)
Show source-provided references
1. Using ML in Burnout Prediction: A survey10.1007/s10560-020-00733-w ↗
2. Workforce optimization for bank operation centers: A machine learning approach10.9781/ijimai.2017.07.002 ↗
3. Employee turnover: A novel prediction solution with effective feature selection
4. MLFramework for multi-level classification of company revenue10.1109/ACCESS.2021.3088874 ↗
5. Chaotic variants of grasshopper optimization algorithm and their application to protein structure prediction10.1007/978-981-13-9263-4_7 ↗
6. River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization10.1016/j.jhydrol.2021.126477 ↗
7. Detection and identification of an employee attrition using MLAlgorithm
8. Implementation of ML to determine the best employees using random forest method10.33005/ijconsist.v2i02.43 ↗
9. Hybrid of K-means clustering and naive bayes classifier for predicting performance of an employee10.21533/pen.v9i2.1898 ↗
10. Prediction of employee attrition using ML and ensemble methods
11. Prediction of employee turnover in organizations using MLAlgorithms
12. Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes10.1002/nag.2891 ↗
13. KoRASA: Pipeline optimization for open-source korean natural language understanding framework based on deep learning10.1155/2021/9987462 ↗
14. A survey on MLTechniques in health care industry
15. Efficient diagnosis of liver disease using support vector machine optimized with crows search algorithm
16. Characteristic parameters of Epoch DL to predict Covid-19 data in Indonesia
17. Supervised MLTechniques and genetic optimization for occupational diseases risk prediction10.1007/s00500-019-04200-2 ↗
18. A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans10.7717/peerj-cs.303 ↗
19. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning10.1038/s41551-021-00699-9 ↗
20. Advances in stochastic programming and robust optimization for supply chain planning10.1016/j.cor.2018.07.027 ↗
21. Brain tumors diagnosis and prediction based on applying the learning metaheuristic optimization techniques of particle swarm, ant colony and bee colony10.1016/j.procs.2019.12.098 ↗
22. Application of ML in predicting performance for computer engineering students: A case study
23. Integrating grasshopper optimization algorithm with local search for solving data clustering problems10.2991/ijcis.d.210203.008 ↗
24. Principal components analysis: An alternative way for removing natural growth trends
25. Texture-based face recognition using grasshopper optimization algorithm and deep convolutional neural network
26. Predicting shared-car use and examining nonlinear effects using gradient boosting regression trees10.1080/15568318.2020.1827316 ↗
27. Grasshopper optimization algorithm: Theory, variants, and applications10.1109/ACCESS.2021.3067597 ↗
28. Consumption patterns and public attitudes toward organic foods: The role of climate change awareness10.1007/978-3-030-78566-6_18 ↗
29. A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models
30. Modeling the map reduce based optimal gradient boosted tree classification algorithm for diabetes mellitus diagnosis system10.1007/s12652-020-02242-1 ↗