Multi-model fusion valuation method for second-hand bulk carriers
At present,the valuation of second-hand ships is susceptible to subjective judgment,and there is incomplete consideration of price-influencing factors in the valuation.To address these problems,the self-factors of ships and the market factors are considered comprehensively,transaction data of second-hand bulk carriers from 2000 to 2022 are collected for experiments,and four machine learning algorithms,decision tree,random forest,XGBoost,and LightGBM,are employed to construct valuation models for second-hand bulk carriers,respectively.To further enhance the valuation accuracy,the latter three models are fused as a weighted average model.The results demonstrate that,the highest coefficient value of determination for a single modle on the test set is 0.879,while the coefficient value of determination for the fusion model is 0.889.This indicates the proposed fusion model can accurately value second-hand bulk carriers.The proposed fusion model can provide more accurate and objective valuation services for second-hand bulk carriers in shipping and related industries.
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