Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning
This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; th...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/1512391 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper presents a new hybrid discriminant analysis method, and this method combines the ideas of linearity and nonlinearity to establish a two-layer discriminant model. The first layer is a linear discriminant model, which is mainly used to determine the distinguishable samples and subsample; the second layer is a nonlinear discriminant model, which is used to determine the subsample type. Numerical experiments on real data sets show that this method performs well compared to other classification algorithms, and its stability is better than the common discriminant models. |
|---|---|
| ISSN: | 1026-0226 1607-887X |