Name
Abstract | ||
|---|---|---|
Recently, Bayesian network classifiers (BNCs) have attracted many researchers because they can produce classification models with dependencies among attributes. From the application viewpoint, however, BNCs sometimes produce models too complicated to interpret easily. In this paper, we propose k-Bayesian network classifier (k-BNC), which is a new method to reconstruct the attribute-dependency relationship from data for health promotion planning. From the health promotion viewpoint, it would be highly advantageous if occupational physicians could make effective plans for employees, and if employees could carry out the plans easily. Therefore, we focus on the attribute dependencies in classification models represented as a directed acyclic graph (DAG), and find the effective attributes by measuring the standardized Kullback-Leibler divergence from parent attributes to their children. In experimental evaluation, we firstly compare the accuracy of k-BNC with that of Naive Bayes Classifiers, and other wellknown Bayesian Networks and structure learning methods (k2 algorithm etc.) on some public datasets. We show that our proposed k-BNC method successfully produces classification models for the prioritization of health promotion plans on our health checkup data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.117 | San Diego, CA |
Keywords | Field | DocType |
time-series segmentation,k-bayesian network classifier,bayesian approach,unsupervised scenario,linear gaussian,fundamental problem,prioritizing health promotion plans,segmentation model,naive bayes classifier,directed acyclic graph,health promotion,classification algorithms,data models,accuracy,bayesian methods,health care,kullback leibler divergence,bayesian network,learning artificial intelligence,directed graphs | Data mining,Data modeling,Computer science,Artificial intelligence,Classifier (linguistics),Naive Bayes classifier,Pattern recognition,Directed acyclic graph,Bayesian network,Statistical classification,Machine learning,Bayesian probability,Health promotion | Conference |
ISBN | Citations | PageRank |
978-0-7695-3495-4 | 1 | 0.37 |
References | Authors | |
12 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken Ueno | 1 | 124 | 13.27 |
| Toshio Hayashi | 2 | 10 | 1.74 |
| Koichiro Iwata | 3 | 10 | 1.74 |
| Nobuyoshi Honda | 4 | 10 | 1.74 |
| Youichi Kitahara | 5 | 11 | 1.41 |
| Topon Kumar | 6 | 73 | 5.07 |