Human Talent Prediction in HRM using C4.5 Classification Algorithm
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 8
Abstract
In HRM, among the challenges for HR professionals is to manage an organization’s talents, especially to ensure the right person for the right job at the right time. Human talent prediction is an alternative to handle this issue. Due to that reason, classification and prediction in data mining which is commonly used in many areas can also be implemented to human talent. There are many classification techniques in data mining techniques such as Decision Tree, Neural Network, Rough Set Theory, Bayesian theory and Fuzzy logic. Decision tree is among the popular classification techniques, which can produce the interpretable rules or logic statement. The generated rules from the selected technique can be used for future prediction. In this article, we present the study on how the potential human talent can be predicted using a decision tree classifier. By using this technique, the pattern of talent performance can be identified through the classificationprocess. In that case, the hidden and valuable knowledge discovered in the related databases will be summarized in the decision tree structure. In this study, we use decision tree C4.5 classification algorithm to generate the classification rules for human talent performance records. inally, the generated rules are evaluated using the unseen data in order to estimate the accuracy of the prediction result.
Authors and Affiliations
Hamidah Jantan , Abdul Razak Hamdan , Zulaiha Ali Othman
Keywords
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