The Feasibility Analysis of the Application
of Ensemble Learning to Operational Assistant
Decision-Making
Xiang Wei and Xueman Fan
(&)
Navy Submarine Academy, Qingdao 266199, China
oucfanxm@163.com
Abstract. It is urgent to develop artificial intelligence technology and extend
the brain of warship commanders with intelligent machines, thus assisting
battlefield situation cognition and decision making. Since the 1990s, ensemble
learning has become a new research focus in the field of machine learning.
Ensemble learning can improve the generalization ability of the classification
algorithms, and achieve the effect of “1 + 1 > 2”. Whether the performance
bottleneck of single classifier can be broken by ensemble learning, so as to
improve the ability of operational assistant decision of warship? This is a topic
worth to discuss. In this paper, we focus on whether the torpedo can find the
warship after the warship launched a torpedo decoy, and transform it into the
two classification problem in machine learning. First, the classification data set
is generated through offline simulation. Secondly, select decision trees as base
classification method and Bagging as the typical of ensemble learning, and then
the performance improvement of ensemble learning over single classifier is
analyzed under ideal conditions. Finally, label noise, small training dataset size
is taken into account, and the comparison experiments between ensemble
learning and single classifier are performed further. The experimental results
verify the feasibility of applying the ensemble learning to operation assistant
decision.
Keywords: Operational assistant decision-making Á Decision tree Á Ensemble
learning Á Bagging
1 Introduction
For warships, the threat mainly comes from heavy torpedoes such as self-guided torpedoes, wire-guided torpedoes, air-dropped torpedoes, and rocket-assisted torpedoes.
Self-propelled acoustic decoy can not only simulate the radiated noise and acoustic
reflection characteristics of warships but also mimic the motion characteristics of
warships, which has great deception to torpedoes. Using self-propelled acoustic decoy
to defend acoustic homing torpedo has become one of the main means of warship
underwater defense. Self-propelled acoustic decoy has become one of the most
important equipments for warship underwater defense. After the warship launches the
decoy, it is very important for the subsequent decision-making of the warship to predict
whether the decoy can successfully exert the jamming effect according to the relative
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 289–297, 2020.
https://doi.org/10.1007/978-981-15-0187-6_33
of Ensemble Learning to Operational Assistant
Decision-Making
Xiang Wei and Xueman Fan
(&)
Navy Submarine Academy, Qingdao 266199, China
oucfanxm@163.com
Abstract. It is urgent to develop artificial intelligence technology and extend
the brain of warship commanders with intelligent machines, thus assisting
battlefield situation cognition and decision making. Since the 1990s, ensemble
learning has become a new research focus in the field of machine learning.
Ensemble learning can improve the generalization ability of the classification
algorithms, and achieve the effect of “1 + 1 > 2”. Whether the performance
bottleneck of single classifier can be broken by ensemble learning, so as to
improve the ability of operational assistant decision of warship? This is a topic
worth to discuss. In this paper, we focus on whether the torpedo can find the
warship after the warship launched a torpedo decoy, and transform it into the
two classification problem in machine learning. First, the classification data set
is generated through offline simulation. Secondly, select decision trees as base
classification method and Bagging as the typical of ensemble learning, and then
the performance improvement of ensemble learning over single classifier is
analyzed under ideal conditions. Finally, label noise, small training dataset size
is taken into account, and the comparison experiments between ensemble
learning and single classifier are performed further. The experimental results
verify the feasibility of applying the ensemble learning to operation assistant
decision.
Keywords: Operational assistant decision-making Á Decision tree Á Ensemble
learning Á Bagging
1 Introduction
For warships, the threat mainly comes from heavy torpedoes such as self-guided torpedoes, wire-guided torpedoes, air-dropped torpedoes, and rocket-assisted torpedoes.
Self-propelled acoustic decoy can not only simulate the radiated noise and acoustic
reflection characteristics of warships but also mimic the motion characteristics of
warships, which has great deception to torpedoes. Using self-propelled acoustic decoy
to defend acoustic homing torpedo has become one of the main means of warship
underwater defense. Self-propelled acoustic decoy has become one of the most
important equipments for warship underwater defense. After the warship launches the
decoy, it is very important for the subsequent decision-making of the warship to predict
whether the decoy can successfully exert the jamming effect according to the relative
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 289–297, 2020.
https://doi.org/10.1007/978-981-15-0187-6_33