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Mining for the Most Certain Predictions from Dyadic Data [从二元数据中挖掘最确定的预测Mining for the Most Certain Predictions from Dyadic Data [从二元数据中挖掘最确定的预测 |
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| 课程网址: | http://videolectures.net/kdd09_deodhar_mmcpdd/ |
| 主讲教师: | Meghana Deodhar |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In several applications involving regression or classification, along with making predictions it is important to assess how accurate or reliable individual predictions are. This is particularly important in cases where due to finite resources or domain requirements, one wants to make decisions based only on the most reliable rather than on the entire set of predictions. This paper introduces novel and effective ways of ranking predictions by their accuracy for problems involving large-scale, heterogeneous data with a dyadic structure, i.e., where the independent variables can be naturally decomposed into three groups associated with two sets of elements and their combination. These approaches are based on modeling the data by a collection of localized models learnt while simultaneously partitioning (co-clustering) the data. For regression this leads to the concept of "certainty lift". We also develop a robust predictive modeling technique that identifies and models only the most coherent regions of the data to give high predictive accuracy on the selected subset of response values. Extensive experimentation on real life datasets highlights the utility of our proposed approaches. |
| 关 键 词: | 异构数据; 本地化模型; 预测建模技术 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：yumf |
| 阅读次数: | 69 |