首页|Civil Aviation Flight University of China Reports Findings in Machine Learning ( A hybrid machine learning-based model for predicting flight delay through aviati on big data)
Civil Aviation Flight University of China Reports Findings in Machine Learning ( A hybrid machine learning-based model for predicting flight delay through aviati on big data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting out of Guanghan, People's Republic of Ch ina, by NewsRx editors, the research stated, "The prediction of flight delays is one of the important and challenging issues in the field of scheduling and plan ning flights by airports and airlines. Therefore, in recent years, we have witne ssed various methods to solve this problem using machine learning techniques." Financial support for this research came from This work was supported by: Resear ch on Smart Methods of Civil Aviation Regulatory Audit. Our news journalists obtained a quote from the research from the Civil Aviation Flight University of China, "In this article, a new method is proposed to addres s these issues. In the proposed method, a group of potential indicators related to flight delay is introduced, and a combination of ANOVA and the Forward Sequen tial Feature Selection (FSFS) algorithm is used to determine the most influentia l indicators on flight delays. To overcome the challenges related to large fligh t data volumes, a clustering strategy based on the DBSCAN algorithm is employed. In this approach, samples are clustered into similar groups, and a separate lea rning model is used to predict flight delays for each group. This strategy allow s the problem to be decomposed into smaller sub-problems, leading to improved pr ediction system performance in terms of accuracy (by 2.49%) and pro cessing speed (by 39.17%). The learning model used in each cluster is a novel structure based on a random forest, where each tree component is opti mized and weighted using the Coyote Optimization Algorithm (COA). Optimizing the structure of each tree component and assigning weighted values to them results in a minimum 5.3% increase in accuracy compared to the conventiona l random forest model. The performance of the proposed method in predicting flig ht delays is tested and compared with previous research."
GuanghanPeople's Republic of ChinaAl gorithmsAviationCyborgsEmerging TechnologiesMachine Learning
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