Fuzzy Naive Bayesian for constructing regulated network with weights

Fuzzy Naive Bayesian for constructing regulated network with weights.

In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted members...

Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of N...

Regulated Activation Weights Neural Network (RAWN)

Constructing Bayesian Network in a Changing World

Although the process of decision-making has been investigated for centuries, only in the last few decades have investigators systematically addressed how decisions are made in a dynamic, real-world environment. One of the most daunting challenges faced by decision support systems is a perpetual change in their environment. Existing decision support methodologies, tools, and frameworks are often...

Fuzzy Shortest-Path Network Problems With Uncertain Edge Weights

This paper presents two new types of fuzzy shortest-path network problems. We consider the edge weight of the network as uncertain, which means that it is either imprecise or unknown. Thus, the first type of fuzzy shortest-path problem uses triangular fuzzy numbers for the imprecise problem. The second type uses level (1 − β, 1 − α) interval-valued fuzzy numbers, which are based on past statist...