Deakin University
Browse

File(s) under permanent embargo

Identifying markov blankets using lasso estimation

journal contribution
posted on 2004-04-22, 00:00 authored by Gang LiGang Li, Honghua Dai, Yiqing Tu
Determining the causal relation among attributes in a domain is a key task in data mining and knowledge discovery. The Minimum Message Length (MML) principle has demonstrated its ability in discovering linear causal models from training data. To explore the ways to improve efficiency, this paper proposes a novel Markov Blanket identification algorithm based on the Lasso estimator. For each variable, this algorithm first generates a Lasso tree, which represents a pruned candidate set of possible feature sets. The Minimum Message Length principle is then employed to evaluate all those candidate feature sets, and the feature set with minimum message length is chosen as the Markov Blanket. Our experiment results show the ability of this algorithm. In addition, this algorithm can be used to prune the search space of causal discovery, and further reduce the computational cost of those score-based causal discovery algorithms.

History

Journal

Lecture notes in computer science

Volume

3056

Issue

2004

Pagination

308 - 318

Publisher

Springer Berlin

Location

Berlin, Germany

ISSN

0302-9743

eISSN

1611-3349

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2004 Springer-Verlag Berlin Heidelberg

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC