Resource Type

Journal Article 157

Conference Videos 3

Year

2024 9

2023 12

2022 7

2021 10

2020 6

2019 14

2018 10

2017 22

2016 7

2015 8

2014 1

2013 6

2012 2

2011 4

2010 7

2009 6

2008 4

2007 6

2006 6

2005 2

open ︾

Keywords

selection 11

Feature selection 8

feature extraction 6

Feature extraction 4

fault diagnosis 4

Dimensionality reduction 3

Machine learning 3

feature 3

CAPP 2

Deep learning 2

Face recognition 2

Feature learning 2

genomic selection 2

site selection 2

wastewater treatment 2

2035 1

2D–3D registration 1

Bemisia tabaci 1

Gossypium genotypes 1

open ︾

Search scope:

排序： Display mode:

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1451-1478 doi: 10.1631/FITEE.2100569

Abstract:

For optimal results, retrieving a relevant feature from a has become a hot topic for researchers involvedway for comprehending and highlighting the multitude of challenges and issues in finding the optimal featureaccuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature

Keywords： Feature selection     High dimensionality     Learning techniques     Microarray dataset    

HTML PDF Collect

Unsupervised feature selection via joint local learning and group sparse regression Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 538-553 doi: 10.1631/FITEE.1700804

Abstract:

Feature selection has attracted a great deal of interest over the past decades.By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improvedBecause label information is expensive to obtain, unsupervised feature selection methods are more widelyThe key to unsupervised feature selection is to find features that effectively reflect the underlyingTo address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

HTML PDF Collect

Afeature selection approach based on a similarity measure for software defect prediction Article

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1744-1753 doi: 10.1631/FITEE.1601322

Abstract: To fully measure the correlation between different features and the class, we present a feature selectionFirst, the feature weights are updated according to the similarity of samples in different classes.Second, a feature ranking list is generated by sorting the feature weights in descending order, and allfeature subsets are selected from the feature ranking list in sequence.selection approaches in terms of classification performance.

Keywords： Software defect prediction     Feature selection     Similarity measure     Feature weights     Feature ranking list    

HTML PDF Collect

A new feature selection method for handling redundant information in text classification None

You-wei WANG, Li-zhou FENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 221-234 doi: 10.1631/FITEE.1601761

Abstract: Feature selection is an important approach to dimensionality reduction in the field of text classificationselection method, which can effectively filter the redundant features.Furthermore, an optimal feature selection (OFS) method is chosen to obtain a feature subset FS1.selection) and the OFS methods.selections, and multilabel feature selection based on maximum dependency and minimum redundancy) while

Keywords： Feature selection     Dimensionality reduction     Text classification     Redundant features     Support vector machine    

HTML PDF Collect

A software defect prediction method with metric compensation based on feature selection and transfer Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: Cross-project software solves the problem of insufficient training data for traditional , and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson method is introduced to address data redundancy, and the based technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software method with based on and . The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.

Keywords： Defect prediction     Feature selection     Transfer learning     Metric compensation    

HTML PDF Collect

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph featureeffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

HTML PDF Collect

Review on ranking and selection: A new perspective

L. Jeff HONG, Weiwei FAN, Jun LUO

Frontiers of Engineering Management 2021, Volume 8, Issue 3,   Pages 321-343 doi: 10.1007/s42524-021-0152-6

Abstract: In this paper, we briefly review the development of ranking and selection (R&S) in the past 70 years,

Keywords： ranking and selection     hypothesis testing     dynamic programming     simulation    

HTML PDF Collect

Control mode selection for modal control of long-span arch bridge

Zhengying LI, Zhengliang LI,

Frontiers of Structural and Civil Engineering 2009, Volume 3, Issue 4,   Pages 401-406 doi: 10.1007/s11709-009-0052-z

Abstract: As for the critical mode selection, an approach based on the maximum modal displacement was presented

Keywords： selection     reduced-order     excitation     long-span     simplified    

HTML PDF Collect

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Frontiers in Energy 2020, Volume 14, Issue 4,   Pages 817-835 doi: 10.1007/s11708-020-0709-9

Abstract: dynamic operating data set with steep slope signals is created based on physics equations and then a featuresimilarity-based learning model with an encoder and a decoder is built and trained to achieve feature

Keywords： gas turbine     dynamic simulation     data-driven     transfer learning     feature similarity    

HTML PDF Collect

FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over data stream Regular Papers

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 388-404 doi: 10.1631/FITEE.1800038

Abstract:

Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because: (1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling; (2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection (FAAD) method which includes three algorithms. First, a method called “information calculation and minimum spanning tree cluster” is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called “random sampling and subsequence partitioning based on the index probabilistic suffix tree.” Last, the method called “anomaly buffer based on model dynamic adjustment” dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit data. Compared with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.

Keywords： Data stream     Multi-dimensional sequence     Anomaly detection     Concept drift     Feature selection    

HTML PDF Collect

Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0737-8

Abstract: This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPsThe weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchyThe measured average sampling surface roughness of the most crucial feature of the bracket in the original

Keywords： selective laser melting (SLM)     build orientation determination     multi-feature mechanical part (MFMP)    

HTML PDF Collect

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 38-42

Abstract: It can extract nonlinear feature components of samples.However, feature extraction for one sample requires that kernel functions between training samples andSo, the size of training sample set affects the efficiency of feature extraction.It is supposed that in feature space the eigenvectors may be linearly expressed by a part of trainingIKPCA extracts feature components of one sample efficiently, only based on kernel functions between nodes

Keywords： KPCA(Kernel PCA)     IKPCA(Improved KPCA)     feature extraction     feature space    

HTML PDF Collect

composition differences between processed protein from different animal species by self-organizing feature

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

Frontiers of Agricultural Science and Engineering 2016, Volume 3, Issue 2,   Pages 171-179 doi: 10.15302/J-FASE-2016095

Abstract: In this study, self-organizing feature maps (SOFM) were used to visualize amino acid composition of fish

Keywords： self-organizing feature maps     visualization     processed animal proteins (PAPs)     amino acid    

HTML PDF Collect

Supplier selection and order splitting in multiple-sourcing inventory systems

WANG Guicong, JIANG Zhaoliang, LI Zhaoqian, LIU Wenping

Frontiers of Mechanical Engineering 2008, Volume 3, Issue 1,   Pages 23-27 doi: 10.1007/s11465-008-0016-3

Abstract: Supplier selection and inventory control are critical decision processes in single-item multiple-supplier

Keywords： automobile industry     branch-bound algorithm     selection     single-item multiple-supplier     effective    

HTML PDF Collect

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 406-419 doi: 10.1007/s11465-017-0419-0

Abstract: several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault featureAiming to extract the fault feature of planet gear effectively, we propose a novel feature extractioncollected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature

Keywords： wind turbine     planet gear fault     feature extraction     spectral kurtosis     time wavelet energy spectrum    

HTML PDF Collect

Title Author Date Type Operation

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Journal Article

Unsupervised feature selection via joint local learning and group sparse regression

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Journal Article

Afeature selection approach based on a similarity measure for software defect prediction

Qiao YU, Shu-juan JIANG, Rong-cun WANG, Hong-yang WANG

Journal Article

A new feature selection method for handling redundant information in text classification

You-wei WANG, Li-zhou FENG

Journal Article

A software defect prediction method with metric compensation based on feature selection and transfer

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Review on ranking and selection: A new perspective

L. Jeff HONG, Weiwei FAN, Jun LUO

Journal Article

Control mode selection for modal control of long-span arch bridge

Zhengying LI, Zhengliang LI,

Journal Article

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Journal Article

FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over data stream

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Journal Article

Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Journal Article

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Journal Article

composition differences between processed protein from different animal species by self-organizing feature

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

Journal Article

Supplier selection and order splitting in multiple-sourcing inventory systems

WANG Guicong, JIANG Zhaoliang, LI Zhaoqian, LIU Wenping

Journal Article

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Journal Article