Application of RS-SVM in Coal Mine Fire Warning System

Abstract:

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.

• Periodical: Key Engineering Materials (Volume 693)
• Pages: 1331-1338
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.693.1331
• Online since: May 2016
• Authors: Hua Ping Zhou*, Bo Jie Xiong, Cheng Jun Wang
• Keywords: Attribute Reduction, Discretization of Attributes, Fire Warning, Information Fusion, Rough Set, Support Vector Machine

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