Title :
Data mining classification technique for talent management using SVM
Author :
Yasodha, S. ; Prakash, P.S.
Author_Institution :
Dept. of Comput. Sci. & Eng., Sona Coll. of Technol., Salem, India
Abstract :
In Human Resource Management (HRM), the top challenge for HR professionals is managing the organizational talents. The talent management problem can be solved using the classification technique in data mining. There are several classification techniques present such as Decision Tree, Neural Networks, Support vector machine (SVM) and nearest neighbour algorithm. In this paper we suggest a combined hybrid approach CACC-SVM for potential classification of HR data. This approach yields better accuracy than the traditional classification algorithms because of concise summarization of continuous attributes through CACC discretization and high performing generalized classifier SVM.
Keywords :
data mining; decision trees; neural nets; pattern classification; support vector machines; HRM; SVM; data mining classification technique; decision tree; human resource management; nearest neighbour algorithm; neural networks; organizational talents; support vector machine; talent management; Classification algorithms; Computational modeling; Forecasting; Kernel; Polynomials; Predictive models; Support vector machines; Class-Attribute Contingency Coefficient (CACC); Classification; Sequential Minimal Optimization (SMO); Support vector machines (SVM); Talent management;
Conference_Titel :
Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on
Conference_Location :
Kumaracoil
Print_ISBN :
978-1-4673-0211-1
DOI :
10.1109/ICCEET.2012.6203768