Research on police performance appraisal early warning methods based on improved decision tree algorithm

被引：0
作者：
机构：
[1] School of Computer Science and Technology, Tianjin University, Nankai District, Tianjin
来源：
| 1600年 / Springer Verlag卷 / 332期
关键词：
Classification; Decision tree; Early warning; Improved ID3 algorithm; Rough set;
D O I：
10.1007/978-3-642-34447-3_51
中图分类号：
学科分类号：
摘要：
With the help of Police Performance Appraisal System, the working quality of the Public Security Organs of Tianjin has been improved. As a part of the system, Police Performance Appraisal Early Warning can accelerate feedback speed and improve the efficiency of the performance appraisal. This paper aims at proposing an improved decision tree classification algorithm based on ID3 algorithm to raise the accuracy of early warning. The attribute selection measure of the improved ID3 algorithm is determined by information gain, the attribute significance and the number of attribute values, which not only overcomes the disadvantage of the gain criterion tends to favor attributes with many values of ID3 algorithm, but also considers the dependence between the attributes. The datasets used by experiments come from the Police Performance Appraisal Early Warning System and the UCI Machine Learning Repository respectively. The comparative analysis of the experimental results shows that the improved ID3 algorithm is more accurate and stable. © Springer-Verlag Berlin Heidelberg 2012.
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页码：571 / 583
页数：12
相关论文
共 18 条
  • [1] Quinlan J.R., Induction of decision trees, Machine Learning, 1, 1, pp. 81-106, (1986)
  • [2] Quinlan J.R., C4.5: Programs For Machine Learning, (1993)
  • [3] Pawlak Z., Information Systems Theoretical Foundations, Information Systems, 6, 3, pp. 205-218, (1981)
  • [4] Wang G., Rough Set Theory and Knowledge Acquisition, (2001)
  • [5] Wang D., Jiang L., An Improved Attribute Selection Measure for Decision Tree Induction, Fourth International Conference On Fuzzy Systems and Knowledge Discovery, FSKD 2007, 4, pp. 654-658, (2007)
  • [6] Ding B., Zheng Y., Zang S., A New Decision Tree Algorithm Based on Rough Set Theory, Asia-Pacific Conference On Information Processing, 2, pp. 326-329, (2009)
  • [7] Guan C., Zeng X., An Improved ID3 Based on Weighted Modified Information Gain, 2011 Seventh International Conference On Computational Intelligence and Security (CIS), pp. 1283-1285, (2011)
  • [8] Rastogl R., Shim K., PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning, Data Mining and Knowledge Discovery, 4, pp. 315-344, (2000)
  • [9] Chandra B., Kothari R., Paul P., A new node splitting measure for decision tree construction, Pattern Recognition, 43, pp. 2725-2731, (2010)
  • [10] Wei J., Wang S., Yu G., Gu L., Wang G., Yuan X., A novel method for pruning decision trees, 2009 International Conference On Machine Learning and Cybernetics, 1, pp. 339-341, (2009)
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