基于机器学习的平面公益广告效果评价研究

Effect Evaluation of Plane Public Service Advertisement Based on Machine Learning

中文关键词: 多特征  多模型  平面公益广告  效果评价研究

英文关键词: multi-feature  multi-model  print public service advertisement  effect evaluation research

中文摘要:

      目的 从数据科学基础理论出发，通过探索基于机器学习的平面公益广告效果评价模型，实现对平面公益广告设计的效果评价。方法 对平面公益广告图像数据集进行切分(7∶3)，并对广告图像特征进行多维度提取，随后采用随机森林叠加多层感知机和XGBoost算法构建集成分类模型，并经训练得到广告效果强弱分类评价模型。结果 测试结果显示，在训练集的F1值为1.0时，该模型在测试集的F1值可达0.897 6，能对广告效果做出正确评价。结论 基于机器学习的多特征融合、多模型集成模型能够实现对平面公益广告效果的准确评价，为平面公益广告效果评价提供一种新方法。

英文摘要:

      The work aims to explore the machine learning-based effect evaluation model of print public service advertisement based on the basic theory of data science, and realize the design effect evaluation of print public service advertisement. Firstly, the image data set of print public service advertisement was segmented according to the ratio of 7∶3, and the characteristics of advertisement images were extracted from multiple dimensions. Then, the classification model was constructed by integrating random forest with Multi-layer Perceptron Model and XGBoost algorithm, and the classification evaluation model of the strong and week advertising effect was obtained through training. The test results showed that the F1 value in the training set was 1.0, and the F1 value in the test set was about 0.897 6, which could correctly evaluate the advertising effect. The multi-feature fusion and multi-model integration model based on machine learning can realize the accurate evaluation of print public service advertisements, and provide a new method for the evaluation of print public service advertisements effect.