The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data

Mehreen Saeed
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:225-236, 2010.

Abstract

This paper describes the use of Bernoulli mixture models for extracting boolean rules from data. Bernoulli mixtures identify high data density areas on the corners of a hypercube. One corner represents a conjunction of literals in a boolean clause and the set of all identified corners, of the hypercube, indicates disjuncts of clauses to form a rule. Further class labels can be used to select features or variables, in the individual conjuncts, that are relevant to the target variable. This method was applied to the SIGNET dataset of the causality workbench challenge. The dataset is derived from a biological signaling network with 21 time steps and 43 random boolean variables. Results indicate that Bernoulli mixtures are quite effective at extracting boolean rules from data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-saeed10a, title = {The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data}, author = {Saeed, Mehreen}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {225--236}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/saeed10a/saeed10a.pdf}, url = {https://proceedings.mlr.press/v6/saeed10a.html}, abstract = {This paper describes the use of Bernoulli mixture models for extracting boolean rules from data. Bernoulli mixtures identify high data density areas on the corners of a hypercube. One corner represents a conjunction of literals in a boolean clause and the set of all identified corners, of the hypercube, indicates disjuncts of clauses to form a rule. Further class labels can be used to select features or variables, in the individual conjuncts, that are relevant to the target variable. This method was applied to the SIGNET dataset of the causality workbench challenge. The dataset is derived from a biological signaling network with 21 time steps and 43 random boolean variables. Results indicate that Bernoulli mixtures are quite effective at extracting boolean rules from data.} }
Endnote
%0 Conference Paper %T The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data %A Mehreen Saeed %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-saeed10a %I PMLR %P 225--236 %U https://proceedings.mlr.press/v6/saeed10a.html %V 6 %X This paper describes the use of Bernoulli mixture models for extracting boolean rules from data. Bernoulli mixtures identify high data density areas on the corners of a hypercube. One corner represents a conjunction of literals in a boolean clause and the set of all identified corners, of the hypercube, indicates disjuncts of clauses to form a rule. Further class labels can be used to select features or variables, in the individual conjuncts, that are relevant to the target variable. This method was applied to the SIGNET dataset of the causality workbench challenge. The dataset is derived from a biological signaling network with 21 time steps and 43 random boolean variables. Results indicate that Bernoulli mixtures are quite effective at extracting boolean rules from data.
RIS
TY - CPAPER TI - The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data AU - Mehreen Saeed BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-saeed10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 225 EP - 236 L1 - http://proceedings.mlr.press/v6/saeed10a/saeed10a.pdf UR - https://proceedings.mlr.press/v6/saeed10a.html AB - This paper describes the use of Bernoulli mixture models for extracting boolean rules from data. Bernoulli mixtures identify high data density areas on the corners of a hypercube. One corner represents a conjunction of literals in a boolean clause and the set of all identified corners, of the hypercube, indicates disjuncts of clauses to form a rule. Further class labels can be used to select features or variables, in the individual conjuncts, that are relevant to the target variable. This method was applied to the SIGNET dataset of the causality workbench challenge. The dataset is derived from a biological signaling network with 21 time steps and 43 random boolean variables. Results indicate that Bernoulli mixtures are quite effective at extracting boolean rules from data. ER -
APA
Saeed, M.. (2010). The Use of Bernoulli Mixture Models for Identifying Corners of a Hypercube and Extracting Boolean Rules From Data. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:225-236 Available from https://proceedings.mlr.press/v6/saeed10a.html.

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