.png){kind=icon}
Return
A Fast Multi-Network K-Dependence Bayesian Classifier for Continuous Features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2024.110299.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
One of the Bayesian network classifiers widely used in the classification is K -dependence Bayesian (KDB). However, most of the KDB classifiers build a single network on a class variable without considering dependencies between features in each class. Moreover, many KDB classifiers need the discretization process to handle continuous features. This paper aims to propose a fast Multi -Network K -Dependence Bayesian (MNKDB) classifier for continuous features. According to this aim, we propose a non -parametric approach that efficiently identifies dependencies between continuous features in each class with a low computational cost and without discretizing continuous features. The results indicate that the MNKDB classifier is more accurate than the state-of-the-art KDB classifiers, especially for datasets with more than three classes. The MNKDB classifier not only decreases the classification time but also deals with continuous variables without discretizing them. The results for K=2 show that the MNKDB classifier is 36.5, 31.8, and 14.2 times faster and 4.13%, 5.15%, and 5.48% more accurate than the state-of-the-art FKDB (Flexible KDB), KMM-KDB (Kernel Mixture Model based on KDB), and SKDB (Scalable KDB) classifiers, respectively.
Keywords:
Kernel density estimation
B-spline functions
Probability density estimation
Continuous features
Bayesian network classifier
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
ShareSaveEvaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSaveShareSaveOn solving a healthcare supplier selection problem using MCDM methods in intuitionistic fuzzy environmentOPSEARCH
OPSEARCHIF0
PREAI
ShareSaveComparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structuresSCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSave
View more