The Regression Learning of the Imbalanced and Big Data by the Online Mixture Model for the Mach Number Forecasting
Extracting valuable information to enhance the performance of forecasting models from the imbalanced and big data requires the scalable implementation of advanced statistical learning methods. This paper proposes the online mixture model (OMM) and applies it to the Mach number forecasting. Treating...
Main Authors: | Xiao-Jun Wang, Yan Liu, Ping Yuan, Chang-Jun Zhou, Lin Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8576504/ |
Similar Items
-
PSU: Particle Stacking Undersampling Method for Highly Imbalanced Big Data
by: Yong-Seok Jeon, et al.
Published: (2020-01-01) -
SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data
by: María José Basgall, et al.
Published: (2018-12-01) -
A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem
by: Terzi Duygu Sinanc, et al.
Published: (2019-12-01) -
Comparing the performance of adaboost, xgboost, and logistic regression for imbalanced data
by: Lai, S.B.S, et al.
Published: (2021) -
Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset
by: Tanujit Chakraborty
Published: (2019-08-01)