scholarly journals A New Feature Selection Algorithm Based on the Mean Impact Variance

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Weidong Cheng ◽  
Tianyang Wang ◽  
Weigang Wen ◽  
Jianyong Li ◽  
Robert X. Gao
Keyword(s):  
Feature Selection ◽  
Back Propagation ◽  
Feature Selection Method ◽  
Principal Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bp Network ◽  
The Mean ◽  
New Feature ◽  
Artificial Neural Network Ann

The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN) algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR) method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1) 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2) the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3) 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA) algorithm and a back propagation (BP) network is constructed, and (4) the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Congwei Sun ◽  
Zhijun Dai ◽  
Hongyan Zhang ◽  
Lanzhi Li ◽  
Zheming Yuan
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Principal Component ◽  
Neuronal Morphology ◽  
Support Vector ◽  
Neuron Type ◽  
Neuronal Function ◽  
Binary Matrix ◽  
New Feature ◽  
Feature Dimension

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained.

A lazy feature selection method for multi-label classification

2021 ◽  
Vol 25 (1) ◽  
pp. 21-34
Author(s):  
Rafael B. Pereira ◽  
Alexandre Plastino ◽  
Bianca Zadrozny ◽  
Luiz H.C. Merschmann
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Selection Method ◽  
Video Classification ◽  
Classification Problems ◽  
Class Label ◽  
New Feature ◽  
Feature Selection Techniques ◽  
Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

A New Feature Selection Method for Driving Fatigue Detection Using EEG Signals

2021 ◽  
pp. 535-542
Author(s):  
Zaifei Luo ◽  
Yun Zheng ◽  
Yuliang Ma ◽  
Qingshan She ◽  
Mingxu Sun ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Eeg Signals ◽  
Driving Fatigue ◽  
New Feature ◽  
Fatigue Detection
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