Feature selection using autoencoders with Bayesian methods to high-dimensional data

被引:3
作者
Shu, Lei [1 ]
Huang, Kun [2 ]
Jiang, Wenhao [1 ]
Wu, Wenming [1 ]
Liu, Hongling [1 ]
机构
[1] Chongqing Aerosp Polytech, Chongqing 400021, Peoples R China
[2] Urban Vocat Coll Sichuan, Chengdu, Peoples R China
关键词
Autoencoder; Bayesian method; feature selection; high-dimensional data; FEEDFORWARD NEURAL-NETWORKS;
D O I
10.3233/JIFS-211348
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is easy to lead to poor generalization in machine learning tasks using real-world data directly, since such data is usually high-dimensional dimensionality and limited. Through learning the low dimensional representations of high-dimensional data, feature selection can retain useful features for machine learning tasks. Using these useful features effectively trains machine learning models. Hence, it is a challenge for feature selection from high-dimensional data. To address this issue, in this paper, a hybrid approach consisted of an autoencoder and Bayesian methods is proposed for a novel feature selection. Firstly, Bayesian methods are embedded in the proposed autoencoder as a special hidden layer. This of doing is to increase the precision during selecting non-redundant features. Then, the other hidden layers of the autoencoder are used for non-redundant feature selection. Finally, compared with the mainstream approaches for feature selection, the proposed method outperforms them. We find that the way consisted of autoencoders and probabilistic correction methods is more meaningful than that of stacking architectures or adding constraints to autoencoders as regards feature selection. We also demonstrate that stacked autoencoders are more suitable for large-scale feature selection, however, sparse autoencoders are beneficial for a smaller number of feature selection. We indicate that the value of the proposed method provides a theoretical reference to analyze the optimality of feature selection.
引用
收藏
页码:7397 / 7406
页数:10
相关论文
共 52 条
[1]   Improving Biochemical Named Entity Recognition Using PSO Classifier Selection and Bayesian Combination Methods [J].
Akkasi, Abbas ;
Varoglu, Ekrem .
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2017, 14 (06) :1327-1338
[2]   Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection [J].
Ang, Jun Chin ;
Mirzal, Andri ;
Haron, Habibollah ;
Hamed, Haza Nuzly Abdull .
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2016, 13 (05) :971-989
[3]  
[Anonymous], 2013, BRAIN STRUCT FUNCT
[4]   Local Feature Selection for Data Classification [J].
Armanfard, Narges ;
Reilly, James P. ;
Komeili, Majid .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (06) :1217-1227
[5]  
Bengio Y., 2013, NIPS
[6]   Representation Learning: A Review and New Perspectives [J].
Bengio, Yoshua ;
Courville, Aaron ;
Vincent, Pascal .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) :1798-1828
[7]  
Brown G, 2012, J MACH LEARN RES, V13, P27
[8]   Feature Selection Using a Neural Framework With Controlled Redundancy [J].
Chakraborty, Rudrasis ;
Pal, Nikhil R. .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (01) :35-50
[9]   Deep sparse feature selection for computer aided endoscopy diagnosis [J].
Cong, Yang ;
Wang, Shuai ;
Liu, Ji ;
Cao, Jun ;
Yang, Yunsheng ;
Luo, Jiebo .
PATTERN RECOGNITION, 2015, 48 (03) :907-917
[10]   Sparse Autoencoder-based Feature Transfer Learning for Speech Emotion Recognition [J].
Deng, Jun ;
Zhang, Zixing ;
Marchi, Erik ;
Schuller, Bjoern .
2013 HUMAINE ASSOCIATION CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII), 2013, :511-516