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ICDM
2010
IEEE

Causal Discovery from Streaming Features

8 years 1 months ago
Causal Discovery from Streaming Features
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available before learning begins. Feature generation and selection often have to be interleaved. Managing streaming features has been extensively studied in classification, but little attention has been paid to the problem of causal discovery from streaming features. To this end, we propose a novel algorithm to solve this challenging problem, denoted as CDFSF (Causal Discovery From Streaming Features) which consists of two phases: growing and shrinking. In the growing phase, CDFSF finds candidate parents or children for each feature seen so far, while in the shrinking phase the algorithm dynamically removes false positives from the current sets of candidate parents and children. In order to improve the efficiency of CDFSF, we present S-CDFSF, a faster version of CDFSF, using two symmetry theorems. Experimental results ...
Kui Yu, Xindong Wu, Hao Wang, Wei Ding
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICDM
Authors Kui Yu, Xindong Wu, Hao Wang, Wei Ding
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