Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.
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DOI：
10.1007/978-3-642-40994-3_38
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Costa, Vitor Santos
中科院分区：
文献类型：
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作者：
Nassif, Houssam;Kuusisto, Finn;Burnside, Elizabeth S;Page, David;Shavlik, Jude;Costa, Vitor Santos
我们介绍了分数，因为你电梯（SAYL），一种新的统计关系学习（SRL）算法，并将其应用到一个重要的任务，在乳腺癌的诊断。SAYL将SRL与提升建模的营销概念相结合，利用提升曲线下的面积来指导条款构建和最终的理论评估，集成规则学习和概率分配，并对每个新的理论规则添加到现有规则中进行条件约束。乳腺癌是女性中最常见的癌症类型，可分为两种亚型：早期原位阶段，癌细胞仍被限制，以及随后的侵袭性阶段。目前，患有原位癌的老年妇女接受治疗以防止癌症进展，尽管治疗可能产生不良副作用，妇女可能死于其他原因。年轻女性往往有更积极的癌症，而老年女性往往有更多的惰性肿瘤。因此，原位肿瘤与年轻女性原位癌显著不同的老年女性不太可能进展，因此可以考虑观察等待。受这个重要问题的启发，这项工作做出了两个主要贡献。首先，我们提出了第一个多关系提升建模系统，并介绍，实现和评估一种新的方法来指导搜索在SRL框架。其次，我们比较我们的算法与以前的方法，并证明该系统确实可以获得感兴趣的差异规则的专家对真实的数据，同时显着提高数据提升。
We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones. Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting. Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift.