Mendeley
TY  _  JOUR
ID  -  139701158306101478
TI  -  A Hybrid-Based Feature Selection Method for High-Dimensional Data Using Ensemble Methods
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Rouhi A.
AU  -  Nezamabadi-pour H.
AD  -  دانشگاه شهيد باهنر کرمان
AD  -  دانشگاه شهید باهنر کرمان
Y1  -  2018
PY  -  2018
VL  -  4
IS  -  
SP  -  283
EP  -  294
KW  -  Feature selection
high-dimensional data
hybrid methods
meta-heuristic methods
filter methods
wrapper methods
ensemble methods
DO  -  
N2  -  Nowadays, with the advent and proliferation of high-dimensional data, the process of feature selection plays an important role in the domain of machine learning and more specifically in the classification task. Dealing with high-dimensional data, e.g. microarrays, is associated with problems such as increased presence of redundant and irrelevant features, which leads to decreased classification accuracy, increased computational cost, and the curse of dimensionality. In this paper, a hybrid method using ensemble methods for feature selection of high dimensional data, is proposed. In the proposed method, in the first stage, a filter method reduces the dimensionality of features and then, in the second stage, two state-of-the-art wrapper methods run on the subset of reduced features using the ensemble technique. The proposed method is benchmarked using 8 microarray datasets. The comparison results with several state-of-the-art feature selection methods confirm the effectiveness of the proposed approach.
UR  - rimag.ir/en/Article/28291
L1  - rimag.ir/en/Article/Download/28291
ER  -  
