Algorithms for Large Scale Markov Blanket Discovery
نویسندگان
چکیده
This paper presents a number of new algorithms for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a low-order polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other state-of-the-art local and global methods with excel-
منابع مشابه
Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data
The importance of Markov blanket discovery algorithms is twofold: as the main building block in constraint-based structure learning of Bayesian network algorithms and as a technique to derive the optimal set of features in filter feature selection approaches. Equally, learning from partially labelled data is a crucial and demanding area of machine learning, and extending techniques from fully t...
متن کاملLocal Causal Discovery of Direct Causes and Effects
We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art causal learning algorithms generally need to find the global causal structures in the form of complete partial directed acyclic graphs (CPDAG) in order to identify direct causes and effects of a target variable. While these algorithms are effective, it is often un...
متن کاملConstruction of Large-Scale Bayesian Networks by Local to Global Search
Most existing algorithms for structural learning of Bayesian networks are suitable for constructing small-sized networks which consist of several tens of nodes. In this paper, we present a novel approach to the efficient and relatively-precise induction of large-scale Bayesian networks with up to several hundreds of nodes. The approach is based on the concept of Markov blanket and makes use of ...
متن کاملLocal Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
In part I of this work we introduced and evaluated the Generalized Local Learning (GLL) framework for producing local causal and Markov blanket induction algorithms. In the present second part we analyze the behavior of GLL algorithms and provide extensions to the core methods. Specifically, we investigate the empirical convergence of GLL to the true local neighborhood as a function of sample s...
متن کاملImproving Structure MCMC for Bayesian Networks through Markov Blanket Resampling
Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov C...
متن کامل