Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

We propose a new scoring function for learning Bayesian networks from data using score+
search algorithms. This is based on the concept of mutual information and exploits some …

Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple
conditions under which discretization is equivalent to use of the true probability density …

This paper re-examines a continuous optimization framework dubbed NOTEARS for
learning Bayesian networks. We first generalize existing algebraic characterizations of …

The project management field has the imperative to increase the success probability of
projects. Experts have developed several Project Management Maturity (PMM) models to …

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and
norepinephrine neurons with a possible role in stress-related disorders. Here we report that …

The interaction among various water cycle components consists of complex, non-linear, and
bidirectional (interdependent) biophysical processes which can be interpreted using …

Numerous studies suggest that built environments have impacts on transit ridership, but few
consider the causal connection between them. The goal of this study is to examine the …

This study proposes and evaluates a new Bayesian network classifier (BNC) having an I-
map structure with the fewest class variable parameters among all structures for which the …