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Data mining classification technique for talent management using SVM

机译:基于支持向量机的人才管理数据挖掘分类技术

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摘要

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 CACCSVM 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.
机译:在人力资源管理(HRM)中,人力资源专业人员面临的最大挑战是管理组织人才。使用数据挖掘中的分类技术可以解决人才管理问题。存在几种分类技术,例如决策树,神经网络,支持向量机(SVM)和最近邻居算法。在本文中,我们建议使用组合的混合方法CACCSVM对HR数据进行潜在分类。由于通过CACC离散化和高性能的广义分类器SVM对连续属性进行了简洁的汇总,因此该方法比传统分类算法具有更高的准确性。

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