Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Abstract Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imb...

Full description

Bibliographic Details
Main Authors: Mahboubeh Fattahi, Mohammad Hossein Moattar, Yahya Forghani
Format: Article
Language:English
Published: SpringerOpen 2022-05-01
Series:Journal of Big Data
Subjects:
Feature selection
Feature extraction
Imbalanced data
Big data classification
Cost sensitive
Optimization
Online Access:https://doi.org/10.1186/s40537-022-00617-z

Similar Items

Similar Items