期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2015
卷号：9
期号：10
页码：425-432
DOI：10.14257/ijsia.2015.9.10.39
出版社：SERSC
摘要：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.
关键词：Coal mining data; Data mining; Class label prediction; Na.ve bays ; classifier; Artificial neural network; Decision tree model
