Classification for talent management using decision tree induction techniques

Hamidah Jantan, Abdul Razak Hamdan, Zulaiha Ali Othman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.

Original languageEnglish
Title of host publication2009 2nd Conference on Data Mining and Optimization, DMO 2009
Pages15-20
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 2nd Conference on Data Mining and Optimization, DMO 2009 - Bangi, Selangor
Duration: 27 Oct 200928 Oct 2009

Other

Other2009 2nd Conference on Data Mining and Optimization, DMO 2009
CityBangi, Selangor
Period27/10/0928/10/09

Fingerprint

Decision trees
Classifiers
Data mining
Genetic algorithms
Neural networks

Keywords

  • C4.5
  • Classification
  • Data mining
  • Decision tree
  • Talent forecasting

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software

Cite this

Jantan, H., Hamdan, A. R., & Ali Othman, Z. (2009). Classification for talent management using decision tree induction techniques. In 2009 2nd Conference on Data Mining and Optimization, DMO 2009 (pp. 15-20). [5341916] https://doi.org/10.1109/DMO.2009.5341916

Classification for talent management using decision tree induction techniques. / Jantan, Hamidah; Hamdan, Abdul Razak; Ali Othman, Zulaiha.

2009 2nd Conference on Data Mining and Optimization, DMO 2009. 2009. p. 15-20 5341916.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jantan, H, Hamdan, AR & Ali Othman, Z 2009, Classification for talent management using decision tree induction techniques. in 2009 2nd Conference on Data Mining and Optimization, DMO 2009., 5341916, pp. 15-20, 2009 2nd Conference on Data Mining and Optimization, DMO 2009, Bangi, Selangor, 27/10/09. https://doi.org/10.1109/DMO.2009.5341916
Jantan H, Hamdan AR, Ali Othman Z. Classification for talent management using decision tree induction techniques. In 2009 2nd Conference on Data Mining and Optimization, DMO 2009. 2009. p. 15-20. 5341916 https://doi.org/10.1109/DMO.2009.5341916
Jantan, Hamidah ; Hamdan, Abdul Razak ; Ali Othman, Zulaiha. / Classification for talent management using decision tree induction techniques. 2009 2nd Conference on Data Mining and Optimization, DMO 2009. 2009. pp. 15-20
@inproceedings{04067693c7bc465589dfb3ea0eef7472,
title = "Classification for talent management using decision tree induction techniques",
abstract = "Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.",
keywords = "C4.5, Classification, Data mining, Decision tree, Talent forecasting",
author = "Hamidah Jantan and Hamdan, {Abdul Razak} and {Ali Othman}, Zulaiha",
year = "2009",
doi = "10.1109/DMO.2009.5341916",
language = "English",
isbn = "9781424449446",
pages = "15--20",
booktitle = "2009 2nd Conference on Data Mining and Optimization, DMO 2009",

}

TY - GEN

T1 - Classification for talent management using decision tree induction techniques

AU - Jantan, Hamidah

AU - Hamdan, Abdul Razak

AU - Ali Othman, Zulaiha

PY - 2009

Y1 - 2009

N2 - Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.

AB - Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.

KW - C4.5

KW - Classification

KW - Data mining

KW - Decision tree

KW - Talent forecasting

UR - http://www.scopus.com/inward/record.url?scp=72449133808&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=72449133808&partnerID=8YFLogxK

U2 - 10.1109/DMO.2009.5341916

DO - 10.1109/DMO.2009.5341916

M3 - Conference contribution

SN - 9781424449446

SP - 15

EP - 20

BT - 2009 2nd Conference on Data Mining and Optimization, DMO 2009

ER -