期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 18, 期 1, 页码 70-81出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2013.2281396
关键词
Classification rules; data mining; fire evacuation; multiobjective evolutionary algorithms; particle swarm optimization
资金
- National Natural Science Foundation [61020106009, 61105073, 61173096, 61272075]
- Zhejiang Provincial Natural Science Foundation of China [R1110679]
In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.
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