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In order to solve the problem of inaccurate subjective evaluation of vehicle driving and the inability of objective evaluation to reflect subjective feelings, an evaluation model based on Stacking ensemble learning method is proposed. First, the acceleration condition characteristics of vehicles are studied and objective evaluation indicators of driverability are defined. Then the evaluation indicators are used as input features to train the Stacking ensemble model. Moreover, the Improved Harris Hawk Optimization algorithm was used to optimize the hyperparameters in the Stacking ensemble model to improve the model prediction performance. Finally, the road test proves that the performance of the HHO-Stacking ensemble model is superior to that of a single machine learning model. The qualification rate of the HHO-Stacking ensemble model is 95%. The HHO-Stacking ensemble model can complete the drivability evaluation more effectively.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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