Fanglue: An Interactive System for Decision Rule Crafting

IF 2.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chen Qian, Shiwei Liang, Zhaoyang Wang, Yin Lou
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引用次数: 0

Abstract

In many applications the training data do not always contain sufficient information to produce high-quality decision rules for standard (end-to-end) rule mining algorithms, and human experts have to incorporate domain knowledge during rule induction in order to get meaningful results. In this work we present Fanglue, a home-grown system inside Alipay, for interactive decision rule crafting. Fanglue is a distributed in-memory system and is highly responsive when processing large-scale datasets. In addition, Fanglue extends the standard representation of a decision rule by introducing disjunctive clauses. Having disjunctive clauses can improve the coverage and robustness of a decision rule, especially for fraud prevention in Fintech applications.
方略:决策规则制作的交互式系统
在许多应用中,训练数据并不总是包含足够的信息来为标准(端到端)规则挖掘算法生成高质量的决策规则,并且人类专家必须在规则归纳过程中结合领域知识以获得有意义的结果。在这项工作中,我们展示了支付宝内部的一个自主开发的系统,用于交互式决策规则的制定。方值是一个分布式内存系统,在处理大规模数据集时具有很高的响应速度。此外,方语通过引入析取子句扩展了决策规则的标准表示。具有析取从句可以提高决策规则的覆盖范围和鲁棒性,特别是对于金融科技应用中的欺诈预防。
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来源期刊
Proceedings of the Vldb Endowment
Proceedings of the Vldb Endowment Computer Science-General Computer Science
CiteScore
7.70
自引率
0.00%
发文量
95
期刊介绍: The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
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