scholarly journals A hybrid feature selection model based on butterfly optimization algorithm: COVID ‐19 as a case study

2021 ◽  
Author(s):  
Ibrahim M. EL‐Hasnony ◽  
Mohamed Elhoseny ◽  
Zahraa Tarek
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Selection Model ◽  
Model Based

scholarly journals Hybrid feature selection model based on machine learning and knowledge graph

2021 ◽  
Vol 2079 (1) ◽  
pp. 012028
Author(s):  
Xiaoqing Peng ◽  
Yong Shuai ◽  
Yaxi Gan ◽  
Yaokai Chen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Selection Model ◽  
Knowledge Graph ◽  
Model Based ◽  
Text Features ◽  
Learning Data

Abstract Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor feature interpretability, this paper proposed a hybrid feature selection model based on machine learning and knowledge graph. By the idea of hybridization, this model used supervised learning algorithms, unsupervised learning algorithms and knowledge graph technology to model from the perspective of data features and text features. Firstly, the data-based feature weights were obtained through the machine learning model, and then the text-based weights were obtained by using the knowledge graph technology, and the weight sets are combined to obtain a feature matrix with good explanatory properties that meets both the data and text features. Finally, the case analysis proves that the method proposed in this paper has good effects and interpretability.

scholarly journals Feature selection model based on clustering and ranking in pipeline for microarray data

2017 ◽  
Vol 9 ◽  
pp. 107-122 ◽  
Author(s):  
Barnali Sahu ◽  
Satchidananda Dehuri ◽  
Alok Kumar Jagadev
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Selection Model ◽  
Model Based

scholarly journals Induction Motor Multiclass Fault Diagnosis Based on Mean Impact Value and PSO-BPNN

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Chun-Yao Lee ◽  
Hong-Yi Ou
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Selection Process ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Back Propagation ◽  
Selection Model ◽  
Current Signal ◽  
Model Based

This paper presents a feature selection model based on mean impact value (MIV) to solve induction motor (IM) fault diagnosis on the current signal. In this paper, particle swarm optimization (PSO) is combined with back propagation neural network (BPNN) to classify the current signal of IM. First, the purpose of this study is to establish IM fault diagnosis system. Additionally, this study proposes a feature selection process that is composed of MIV, whose objective is to reduce the number of classifier input features. Secondly, the features are extracted as a feature database after analyzing the current signal of IM, and the fault diagnosis is established through the model of PSO-BPNN. Finally, redundant features are deleted through this feature selection process and a classifier is built. The result shows that the feature selection model based on MIV can filter the features effectively at a signal to noise ratio of 30 dB and 20 dB for the IM fault detection problem. In addition, the computing time of BPNN is also reduced which is helpful for online detection.

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