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A Feature Extraction Method Based on BSO Algorithm for Flight Data

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Book cover Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

Abstract

The feature extraction problem for flight data has aroused increasing attention in the practical and the academic aspects. It can reveal the inherent correlation relation among different parameters for the conditional maintenance of the aircraft. However, the high-dimensional and continuous features in the real number field bring challenges to the extraction algorithms for flight data. Brain Storm Optimization (BSO) algorithm can acquire the optimal solutions by continuously converging and diverging the solution set. In this chapter, a feature extraction method based on BSO algorithm is proposed to mine the associate rules from flight data. By using the designed real-number encoding strategy, the intervals and rule template can be handled directly without data discretization and rule template preset processes. Meanwhile, as the frequent item generation process is unnecessary in our proposed algorithm, the time and space complexity will be reduced simultaneously. In addition, we design the fitness function using support, confidence and length of the rules for the purpose of extracting more practical and intelligible rules without predetermining the parameter thresholds. Besides, high-dimensional problems can also be solved using our algorithm. The experiments using substantial flight data are conducted to illustrate the excellent performance of the proposed BSO algorithm comparing to the Apriori algorithm and Genetic algorithm (GA). Furthermore, the classification problems with two datasets from UCI database are also used to verify the practicability and universality of the proposed method based on BSO algorithm.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant No. 61671041, 61101153, 61806119, 61773119, 61703256, and 61771297; in part by the Shenzhen Science and Technology Innovation Committee under grant number ZDSYS201703031748284; and in part by the Fundamental Research Funds for the Central Universities under Grant GK201703062.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Hui Lu & Chongchong Guan

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Shi Cheng

  3. Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuhui Shi

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  1. Hui Lu
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  2. Chongchong Guan
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  3. Shi Cheng
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  4. Yuhui Shi
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Correspondence to Hui Lu .

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Editors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Lu, H., Guan, C., Cheng, S., Shi, Y. (2019). A Feature Extraction Method Based on BSO Algorithm for Flight Data. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_7

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