Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
ACM SIGKDD Explorations Newsletter
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Machine Learning
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Exact model averaging with naive Bayesian classifiers
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Identifying Markov Blankets with Decision Tree Induction
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Learning Bayesian Networks
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Bounding the false discovery rate in local Bayesian network learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The Journal of Machine Learning Research
Algorithms for discovery of multiple markov boundaries: application to the molecular signature multiplicity problem
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Artificial Intelligence in Medicine
The Journal of Machine Learning Research
Learning patient-specific predictive models from clinical data
Journal of Biomedical Informatics
Introduction to Causal Inference
The Journal of Machine Learning Research
Permutation testing improves Bayesian network learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Learning Instance-Specific Predictive Models
The Journal of Machine Learning Research
Efficiently approximating Markov tree bagging for high-dimensional density estimation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Feature subset selection with cumulate conditional mutual information minimization
Expert Systems with Applications: An International Journal
Editorial: Occupation inference through detection and classification of biographical activities
Data & Knowledge Engineering
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning from mixture of experimental data: a constraint---based approach
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
A Bayesian stochastic search method for discovering Markov boundaries
Knowledge-Based Systems
Divergence-based feature selection for separate classes
Neurocomputing
An experimental comparison of hybrid algorithms for bayesian network structure learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
Adaptive thresholding in structure learning of a Bayesian network
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Intelligent Data Analysis
Profit optimizing customer churn prediction with Bayesian network classifiers
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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We present an algorithmic framework for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large data sets with relatively small samples. The selected feature sets can be used for causal discovery and classification. The framework (Generalized Local Learning, or GLL) can be instantiated in numerous ways, giving rise to both existing state-of-the-art as well as novel algorithms. The resulting algorithms are sound under well-defined sufficient conditions. In a first set of experiments we evaluate several algorithms derived from this framework in terms of predictivity and feature set parsimony and compare to other local causal discovery methods and to state-of-the-art non-causal feature selection methods using real data. A second set of experimental evaluations compares the algorithms in terms of ability to induce local causal neighborhoods using simulated and resimulated data and examines the relation of predictivity with causal induction performance. Our experiments demonstrate, consistently with causal feature selection theory, that local causal feature selection methods (under broad assumptions encompassing appropriate family of distributions, types of classifiers, and loss functions) exhibit strong feature set parsimony, high predictivity and local causal interpretability. Although non-causal feature selection methods are often used in practice to shed light on causal relationships, we find that they cannot be interpreted causally even when they achieve excellent predictivity. Therefore we conclude that only local causal techniques should be used when insight into causal structure is sought. In a companion paper we examine in depth the behavior of GLL algorithms, provide extensions, and show how local techniques can be used for scalable and accurate global causal graph learning.