Application of RS-SVM in coal mine fire warning system

作者：Zhou, Huaping^*; Xiong, Bojie; Wang, Chengjun

来源：Key Engineering Materials, 2016, 693: 1331-1338.

DOI：10.4028/www.scientific.net/KEM.693.1331

• Accurate prediction
• Attribute reduction
• Continuous attribute
• Discretizations
• False alarm rate
• Feature description
• Forecast algorithm
• Information fusion technology

In order to reduce the false alarm rate in coal mine fire warning system, we apply information fusion technology to the system and propose a fire forecast algorithm based on Rough Set Support Vector Machine (RS-SVM). Firstly, we map the feature description of coal mine fires to the knowledge representation system described by rough set;Secondly, we discrete the continuous attributes and eliminate the redundant information for attribute reduction to form a rule set of this knowledge representation system;At last, we use the above rule set as the training sample to optimize the parameters for the fire warning support vector machine. The experimental results show that the accuracy of the algorithm is very high. It can make timely and accurate prediction of coal mine fire.

• 出版日期2016
• 单位安徽大学