Efficiency Of New Feature Selection Method Based On Neural Network

被引：0

作者：

Challita, Nicole ^{[1
,3
]}

Khalil, Mohamad ^{[1
,2
]}

Beauseroy, Pierre ^{[3
]}

机构：

[1] Lebanese Univ, EDST, Azm Res Ctr Biotechnol, Tripoli, Libya

[2] Lebanese Univ, Fac Engn, CRSI Res Ctr, Beirut, Lebanon

[3] Univ Technol Troyes, ICD LM2S, UMR, 12 Rue Marie Curie,CS42060, Troyes, France

来源：

2018 INTERNATIONAL CONFERENCE ON COMPUTER AND APPLICATIONS (ICCA) | 2018年

关键词：

feature selection; neural networks; machine learning; optimization; performance;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In order to monitor a system, the number of measurements and features gathered can be huge. But it is desirable to keep only the important features to reduce the processing demand. The problem is therefore to select a subset of features to obtain the best possible classification performance. In this purpose, many feature selection algorithms have been proposed. In a previous work, we have proposed a new feature selection method inspired by neural network and machine learning. This new method selects the best features using sparse weights of the input features in the neural network. In this paper, we study the performance of this method on simulated data.

引用

页码：317 / 320

页数：4

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