Application of Data Mining Classification in Employee Performance Prediction

@article{Kirimi2016ApplicationOD,
  title={Application of Data Mining Classification in Employee Performance Prediction},
  author={John M. Kirimi and Christopher A. Moturi},
  journal={International Journal of Computer Applications},
  year={2016},
  volume={146},
  pages={28-35}
}
In emerging knowledge economies such as Kenya, organizations rely heavily on their human capital to build value. Consequently, performance management at the individual employee level is essential and the business case for implementing a system to measure and improve employee performance is strong. Data Mining can be used for knowledge discovery of interest in Human Resources Management (HRM). We used the Data Mining classification technique for the extraction of knowledge significant for… 

Figures and Tables from this paper

Personnel selection and prediction of organizational positions using data mining algorithms (case study: Mammut industrial complex)

An empirical study to identify and employ qualified individuals and assign different organizational positions and shows that the organizational position feature has a great impact on forecasting of selection or rejection.

Hybrid of K-means clustering and naive Bayes classifier for predicting performance of an employee

  • Z. M. Fadhil
  • Business, Computer Science
    Periodicals of Engineering and Natural Sciences (PEN)
  • 2021
The proposed framework incorporates the K-Means clustering approach and the Naive Bayes (NB) classification for better results in processing performance data of employees, implemented in WEKA, which enables personnel professionals and decision-makers to predict and optimize their employees' performance.

Discovering Performance Evaluation Features of faculty Members using Data Mining Techniques to Support Decision Making

  • Amani M.S. Salama
  • Computer Science
    International Journal of Computer Applications
  • 2019
A model based on data mining in educational sector to understand the factors that affect faculty members performance is proposed and a classification algorithm is applied to predict the decision needs to be taken for coming staff.

EMPLOYEE PERFORMANCE AND LEAVE MANAGEMENT USING DATA MINING TECHNIQUE

The aim of this paper is set references and guidelines for performance analysis of employees for a typical sales company by using Bayesian Classification algorithm for evaluating the performance of employees of the company.

Machine Learning Framework for Multi-Level Classification of Company Revenue

The experimental results indicate, the XGBoost classifier displayed the best classification performance among the three algorithms used in this study, and the proposed framework has strength in terms of both classification and practical implementation.

Envisaging Employee Churn Using MCDM and Machine Learning

An approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) method is provided.

Use Bagging Algorithm to Improve Prediction Accuracy for Evaluation of Worker Performances at a Production Company

  • H. Saad
  • Computer Science, Business
  • 2018
According to the results, management of the company should take a logic decision about the evaluation of production process and extract the important variables that impact the evaluation.

Assessing Employee Attrition Using Classifications Algorithms

This study uses IBM HR data set and applies different classification methods, such as Support Vector Machine (SVM), Random Forest, J48, LogitBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Bagging, AdaBoost, Logistic Regression, to predict the employee attrition, and observes that data mining methods can be useful for predicting the Employee attrition.

Measuring Factors of Employment by Classification Tree Models

The study analyzes data from a national household survey, which provides information about Irish labour and unemployment status of the respondents, to conclude that a carefully trained classification tree can outperform neural networks trained on the same data in terms of accuracy, but underperforms neural nets interms of AUC.

Employees Performance Prediction

  • B. Prasanthi
  • Business
    International Journal of Scientific Research in Science and Technology
  • 2021
The main objective of this prediction model constructed in this paper is to assist HR personnel in decision making by predicting the performance of an employee.

References

SHOWING 1-10 OF 27 REFERENCES

Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance

To build the classification model the CRISP-DM data mining methodology was adopted and decision tree was the main data mining tool used to build the classified model, where several classification rules were generated.

Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application

This study suggests the potential HR system architecture for talent forecasting by using past experience knowledge known as Knowledge Discovery in Database (KDD) or Data Mining.

Data mining techniques for better decisions in human resource management systems

The paper demonstrates the ability of data mining in improving the quality of the decision-making process in HRMS and gives propositions regarding whether data-mining capabilities should lead to increased performance to sustain competitive advantage.

Human Talent Prediction in HRM using C4.5 Classification Algorithm

The study on how the potential human talent can be predicted using a decision tree classifier is presented, which uses decision tree C4.5 classification algorithm to generate the classification rules for human talent performance records.

Towards applying Data Mining Techniques for Talent Mangement

An overview of some of the talent management problems that can be solved by using Data mining techniques is presented and the potential Data Mining Techniques for talent forecasting are proposed.

The Survey of Data Mining Applications And Feature Scope

In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware

Domain driven data mining in human resource management: A review of current research

PREDICTING FACULTY DEVELOPMENT TRAININGS AND PERFORMANCE USING RULE-BASED CLASSIFICATION ALGORITHM

Results show that professional trainings are needed in order to prepare faculty members to perform their tasks effectively, and significant models needed for predictive analysis of newly-hired faculty members were discovered.

Survey of Classification Techniques in Data Mining

The goal of this survey is to provide a comprehensive review of different classification techniques in data mining based on decision tree, rule based Algorithms, neural networks, support vector machines, Bayesian networks, and Genetic Algorithm and Fuzzy logic.