Abstract：Given that the wireless communication between fishing boats far away from the coast and the shore will be seriously affected in case of emergency situations such as bad weather or natural disasters at sea in the complex and volatile marine environment, a classification algorithm based on decision tree integration algorithm was proposed to locate the fishing boats with the best communication conditions according to the classification results. First, data preprocessing was performed on the data set containing 200 fishing boats. Then based on the decision tree C4.5 algorithm, the four attributes including the signal strength of the fishing boat itself and its environmental conditions (wind speed, sea surface temperature, and sea surface pressure)were extracted as classification attributes to classify the data set. Finally, the Bagging algorithm and Boosting algorithm in ensemble learning were used to build strong classifiers to improve classification performance. The performance comparison results showed that the ensemble learning algorithm can improve the classification performance of the decision tree and the decision tree with Boosting algorithm has the highest classification accuracy of 95.76%. It has been proved that this method can help to quickly and accurately locate one or more fishing boats with the optical communication singals, through which, information can be conveyed to the target fishing vessels in the distance and the quality of maritime wireless communications can be guaranteed
邵旻晖，张琳，周凡. 基于决策树的最佳通信渔船选择方法[J]. 渔业现代化杂志, 2018, 45(3): 49-.
SHAO Minhui, ZHANG Lin, ZHOU Fan. A method to select the fishing boats with optimal communication signals based on decision tree. , 2018, 45(3): 49-.