|
|
 |
| |
|
|
|
Classification Algorithm based on NB for Class Overlapping Problem |
|
|
|
PP: 409-415 |
|
|
|
Author(s) |
|
|
|
Haitao Xiong,
Ming Li,
Tongqiang Jiang,
Shouxiang Zhao,
|
|
|
|
Abstract |
|
|
| Class overlapping is thought as one of the toughest problems in data mining because the complex structure of data. The
current classification algorithms show little consideration of this problem. So when using this traditional classification algorithms to
resolve this problem, classification performance is not good for samples in overlapping region. To meet this critical challenge, in this
paper, we pay a systematic study on the class overlapping problem and propose a new classification algorithm based on NB for class
overlapping problem (CANB). CANB uses NB to find class overlapping region and use this region and non-overlapping region in
NB classification model learning separately. Experimental results on bench mark and real-world data sets demonstrate that CANB can
improve the classification performances for class overlapping problem stably and effectively. |
|
|
|
|
|
|
|
