关键词: 机器学习, Wide&Deep模型, 小样本, 指标预测, 特征交叉, 卷烟焦油

Abstract:

Aiming at the problem that the historical cigarette data samples in the cigarette tar index prediction task have the characteristics of small sample and high dimension， which leads to the low prediction accuracy of the model， a cigarette tar index prediction model based on the improved Wide&Deep was proposed. First， the data samples were predicted through multiple machine learning models and the obtained results were used as new features of the model. Then the new features obtained by the machine learning models were input to the Wide side of the Wide&Deep model，the fusion features were constructed and input to the Deep side of the Wide&Deep model， and by introducing second-order features and attention mechanism to build an attention feature intersection layer， high-order combination of features were achieved to improve the accuracy of model prediction. Experimental results show that compared with the unimproved Wide&Deep model， the proposed model reduces Mean Absolute Error （MAE） by 23.4% and Root Mean Square Error （RMSE） by 21.8%； compared with the Wide&Deep model based on convolutional neural network for extraction features， the proposed model reduces MAE by 15.0% and RMSE by 16.4%. The proposed model effectively improves the accuracy of the cigarette tar index prediction task.

Key words: machine learning, Wide&Deep model, small sample, index prediction, feature intersection, cigarette tar