View in EDS

AN ENSEMBLE FRAMEWORK OF STACKED MULTI REGRESSORS AND GRADIENT BOOSTING ALGORITHMS WITH TUNED HYPER PARAMETERS – ENSTAR TO PREDICT EMPLOYEE PERFORMANCE SCORE USING HR ANALYTICS

Saved in:
Bibliographic Details
Title: AN ENSEMBLE FRAMEWORK OF STACKED MULTI REGRESSORS AND GRADIENT BOOSTING ALGORITHMS WITH TUNED HYPER PARAMETERS – ENSTAR TO PREDICT EMPLOYEE PERFORMANCE SCORE USING HR ANALYTICS
Authors: null Philomine Roseline
Source: International Journal of Applied Mathematics. 38:283-303
Publisher Information: Academic Publications, 2025.
Publication Year: 2025
Description: Machine learning (ML) supervised algorithms are immensely used in predictive analytics handling both categorical and numerical features. HR analytics is one such domain where complete data of employee details – known as historical data, are maintained which can be analysed to study the pattern of employee’s performance, attrition, absenteeism, career succession etc. to predict future outcomes. Regression and Gradient Boosting (GB) algorithms have proved to be versatile taking continuous values as input and predict the output. The objective of this research paper is to use the available HR data set from Kaggle, pre-process them, analyse the features’ importance and apply various regression algorithms as well as on the proposed ensemble stacking of regressors – EnStaR, to predict the employee performance score. Evaluation metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Coefficient of Determination (R2) are calculated and compared with all the individual algorithms as well as EnStaR to assert efficiency of the proposed model.
Document Type: Article
ISSN: 1314-8060
DOI: 10.12732/ijam.v38i3s.146
Accession Number: edsair.doi...........da22f1efdbb2f190c5890b6f05db15b1
Database: OpenAIRE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Roseline%20nP
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsair
DbLabel: OpenAIRE
An: edsair.doi...........da22f1efdbb2f190c5890b6f05db15b1
RelevancyScore: 1052
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1052.22912597656
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: AN ENSEMBLE FRAMEWORK OF STACKED MULTI REGRESSORS AND GRADIENT BOOSTING ALGORITHMS WITH TUNED HYPER PARAMETERS – ENSTAR TO PREDICT EMPLOYEE PERFORMANCE SCORE USING HR ANALYTICS
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22null+Philomine+Roseline%22">null Philomine Roseline</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>International Journal of Applied Mathematics</i>. 38:283-303
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Academic Publications, 2025.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Machine learning (ML) supervised algorithms are immensely used in predictive analytics handling both categorical and numerical features. HR analytics is one such domain where complete data of employee details – known as historical data, are maintained which can be analysed to study the pattern of employee’s performance, attrition, absenteeism, career succession etc. to predict future outcomes. Regression and Gradient Boosting (GB) algorithms have proved to be versatile taking continuous values as input and predict the output. The objective of this research paper is to use the available HR data set from Kaggle, pre-process them, analyse the features’ importance and apply various regression algorithms as well as on the proposed ensemble stacking of regressors – EnStaR, to predict the employee performance score. Evaluation metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Coefficient of Determination (R2) are calculated and compared with all the individual algorithms as well as EnStaR to assert efficiency of the proposed model.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Article
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1314-8060
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.12732/ijam.v38i3s.146
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsair.doi...........da22f1efdbb2f190c5890b6f05db15b1
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsair&AN=edsair.doi...........da22f1efdbb2f190c5890b6f05db15b1
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.12732/ijam.v38i3s.146
    Languages:
      – Text: Undetermined
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 283
    Titles:
      – TitleFull: AN ENSEMBLE FRAMEWORK OF STACKED MULTI REGRESSORS AND GRADIENT BOOSTING ALGORITHMS WITH TUNED HYPER PARAMETERS – ENSTAR TO PREDICT EMPLOYEE PERFORMANCE SCORE USING HR ANALYTICS
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: null Philomine Roseline
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 24
              M: 09
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 13148060
            – Type: issn-locals
              Value: edsair
            – Type: issn-locals
              Value: edsairFT
          Numbering:
            – Type: volume
              Value: 38
          Titles:
            – TitleFull: International Journal of Applied Mathematics
              Type: main
ResultId 1