Bayesian Network Learning via Topological Order

نویسندگان

  • Young Woong Park
  • Diego Klabjan
چکیده

We propose a mixed integer programming (MIP) model and iterative algorithms based on topological orders to solve optimization problems with acyclic constraints on a directed graph. The proposed MIP model has a significantly lower number of constraints compared to popular MIP models based on cycle elimination constraints and triangular inequalities. The proposed iterative algorithms use gradient descent and iterative reordering approaches, respectively, for searching topological orders. A computational experiment is presented for the Gaussian Bayesian network learning problem, an optimization problem minimizing the sum of squared errors of regression models with L1 penalty over a feature network with application of gene network inference in bioinformatics.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

متن کامل

Learning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

متن کامل

Better Initialization Heuristics for Order-based Bayesian Network Structure Learning

An effective approach for learning Bayesian network structures is to perform a local search on the space of topological orderings, followed by a systematic search of compatible parent sets. Typically, the local search is initialized with an ordering generated uniformly at random. This can lead to poor local optima, slow down convergence and hurt the performance of the method. In this work we de...

متن کامل

DisTriB: Distributed Trust Management Model Based on Gossip Learning and Bayesian Networks in Collaborative Computing Systems

The interactions among peers in Peer-to-Peer systems as a distributed collaborative system are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in such uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough inform...

متن کامل

Improving Acyclic Selection Order-Based Bayesian Network Structure Learning

An effective approach for learning Bayesian network structures in large domains is to perform a local search on the space of topological orderings. As with most local search approaches, the quality of the procedure depends on the initialization strategy. Usually, a simple random initialization is adopted. Perez and Mauá developed initialization heuristics that were empirically shown to improve ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Journal of Machine Learning Research

دوره 18  شماره 

صفحات  -

تاریخ انتشار 2017