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.

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