Earticle

초록

영어

For issue that coal mining data tends to be incomplete, noisy and inconsistent, some popular classifiers are applied to predict class label of coal mining dataset. Noise and bad points are rejected from coal mining data which will be exchanged to input format suitable for mining. Then different classifiers are used to classify class label after extracting features. In the end, classification results are analyzed and knowledge assimilation is done. Experiment results show that decision tree model gives 88% of accuracy to correctly predict class label whereas neural network model predicts 85% correct class label. This research provides a powerful class label prediction tool as well as increasing knowledge of data classification models.

목차

Abstract
 1. Introduction
 2. Coal Data Mining
  2.1 Coal Data Mining Process
  2.2 Data Acquisition
 3. Classifier
  3.1 Nearest Neighbor Classifier
  3.2 Decision Tree
  3.3 Bayesian Network
  3.4 Artificial Neural Network
 4. Experimental Result
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Haixu Xi [ Jiangsu University of Technology, No.1801, Zhongwu Avenue, Changzhou City, Jiangsu Province, China ]
• Dan Guo [ Jiangsu University of Technology, No.1801, Zhongwu Avenue, Changzhou City, Jiangsu Province, China ]
• Hongjin Zhu [ Jiangsu University of Technology, No.1801, Zhongwu Avenue, Changzhou City, Jiangsu Province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605