A Matrix-Based Genetic Algorithm for Structure Learning of Bayesian Networks

Abstract

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

Semi-Supervised Recursive Learning of Discriminative Mixture Models for Time-Series Classification

“…The original dataset provided high-quality 3D motion capture features on which most of the competing methods performed equally well. To make the classification task more challenging, we considered two modifications: (1) From the original 1-cycle gait sequence, we took sub-sequences randomly where the starting positions were chosen uniformly at random and the lengths were around 100. (2) Only the features related to the lower body part were used: the joint angles of the torso-femur, femur-tibia, and tibia-foot.…”

“…In a number of data-driven modeling tasks, a generative probabilistic model such as a Bayesian network (BN) is an attractive choice, advantageous in various aspects including the ability to easily incorporate domain knowledge, factorize complex problems into self-contained models, handle missing data and latent factors, and offer interpretability to results, to name a few [1,2]. While such models are implicitly employed for joint density estimation, for the last few decades they have gained significant attention as classifiers.…”