NDLI: Rule induction for uncertain data

Content Provider	Springer Nature Link
Author	Qin, Biao Xia, Yuni Prabhakar, Sunil
Copyright Year	2010
Abstract	Data uncertainty are common in real-world applications and it can be caused by many factors such as imprecise measurements, network latency, outdated sources and sampling errors. When mining knowledge from these applications, data uncertainty need to be handled with caution. Otherwise, unreliable or even wrong mining results would be obtained. In this paper, we propose a rule induction algorithm, called uRule, to learn rules from uncertain data. The key problem in learning rules is to efficiently identify the optimal cut points from training data. For uncertain numerical data, we propose an optimization mechanism which merges adjacent bins that have equal classifying class distribution and prove its soundness. For the uncertain categorical data, we also propose a new method to select cut points based on possible world semantics. We then present the uRule algorithm in detail. Our experimental results show that the uRule algorithm can generate rules from uncertain numerical data with potentially higher accuracies, and the proposed optimization method is effective in the cut point selection for both certain and uncertain numerical data. Furthermore, uRule has quite stable performance when mining uncertain categorical data.
Starting Page	103
Ending Page	130
Page Count	28
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	29
Issue Number	1
e-ISSN	02193116
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-08-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rule Uncertain data Data classification Possible world semantics Business Information Systems Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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