Co-training based virtual sample generation for solving the small sample size problem in process industry

Deep Embedded Multi-view Clustering with Collaborative Training

(2021) Jie Xu et al.   INFORMATION SCIENCES

Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system

(2021) Zhong-Sheng Chen et al.   Soft Computing

Novel Space Projection Interpolation Based Virtual Sample Generation for Solving the Small Data Problem in Developing Soft Sensor

(2021) Qun-Xiong Zhu et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data

(2021) Yan-Lin He et al.   ISA TRANSACTIONS

Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples

(2020) Yan-Lin He et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Novel Virtual Sample Generation Based on Locally Linear Embedding for Optimizing the Small Sample Problem: Case of Soft Sensor Applications

(2020) Qun-Xiong Zhu et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Novel manifold learning based virtual sample generation for optimizing soft sensor with small data

(2020) Xiao-Han Zhang et al.   ISA TRANSACTIONS

A virtual sample generation approach based on a modified conditional GAN and centroidal Voronoi tessellation sampling to cope with small sample size problems: Application to soft sensing for chemical process

(2020) Zhong-Sheng Chen et al.   APPLIED SOFT COMPUTING

Data-Driven Modeling Based on Two-Stream ${\rm{\lambda }}$ Gated Recurrent Unit Network With Soft Sensor Application

(2019) Ruimin Xie et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

An alternative SMOTE oversampling strategy for high-dimensional datasets

(2018) Sebastián Maldonado et al.   APPLIED SOFT COMPUTING

Bayesian network data imputation with application to survival tree analysis

(2016) Paola M.V. Rancoita et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process

(2016) Saeid Shokri et al.   Journal of the Taiwan Institute of Chemical Engineers

Co-training partial least squares model for semi-supervised soft sensor development

(2015) Liang Bao et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Gray bootstrap method for estimating frequency-varying random vibration signals with small samples

(2013) Yanqing Wang et al.   Chinese Journal of Aeronautics

A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities

(2013) Che-Jung Chang et al.   COMPUTERS & INDUSTRIAL ENGINEERING

A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems

(2011) Der-Chiang Li et al.   EXPERT SYSTEMS WITH APPLICATIONS

Multiple-View Multiple-Learner Semi-Supervised Learning

(2011) Shiliang Sun et al.   NEURAL PROCESSING LETTERS