Name
Title | ||
|---|---|---|
Gender classification with cortical thickness measurement from magnetic resonance imaging by using a feature selection method based on evolutionary hypernetworks |
Abstract | ||
|---|---|---|
Hypernetworks are a weighted hypergraph where evolutionary methods are learning the model structure and parameters. The evolutionary methods enable the hypernetwork model to conserve significant features implicitly during the learning process. In this study, we propose a novel feature selection method based on occurrence frequencies of attributes in hyperedges by analyzing the structure of a hypernetwork. We also apply the evolutionary hypernetwork with the proposed feature selection method to the gender classification based on cortical thickness measurement on healthy young adults from Magnetic Resonance Imaging (MRI). The experimental results show that the proposed selection method improves the classification accuracy by approximately 20%. Also, a comparative study on four classification algorithms and three feature selection methods shows that the hypernetwork model with the proposed feature selection method achieves a competitive classification performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FUZZY.2009.5277402 | FUZZ-IEEE |
Keywords | Field | DocType |
novel feature selection method,feature selection method,classification accuracy,classification algorithm,evolutionary hypernetwork,magnetic resonance imaging,cortical thickness measurement,proposed feature selection method,competitive classification performance,hypernetwork model,gender classification,evolutionary method,evolutionary hypernetworks,proposed selection method,accuracy,data mining,support vector machines,feature selection,young adult,comparative study,image classification,classification algorithms,learning artificial intelligence,magnetic resonance image,graph theory,evolutionary computation | Graph theory,Feature selection,Pattern recognition,Computer science,Hypergraph,Support vector machine,Evolutionary computation,Artificial intelligence,Contextual image classification,Statistical classification,Machine learning,Magnetic resonance imaging | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung-Woo Ha | 1 | 216 | 25.36 |
| Joon Hwan Jang | 2 | 7 | 1.81 |
| Do-Hyung Kang | 3 | 13 | 1.68 |
| Wi Hoon Jung | 4 | 4 | 1.80 |
| Jun Soo Kwon | 5 | 67 | 7.79 |
| Byoung-Tak Zhang | 6 | 1571 | 158.56 |