Causal discovery from streaming features
Date
2010
Authors
Yu, K.
Wu, X.
Wang, H.
Ding, W.
Editors
Webb, G.I.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings - IEEE International Conference on Data Mining, 2010 / Webb, G.I. (ed./s), iss.5694102, pp.1163-1168
Statement of Responsibility
Conference Name
10th IEEE International Conference on Data Mining (14 Dec 2010 - 17 Dec 2010 : Sydney, Australia)
Abstract
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 validate our algorithms in comparison with other state-of-art algorithms of causal discovery
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2010 IEEE