A Normal Distribution-Based Over-Sampling Approach to Imbalanced Data Classification

Huaxiang Zhang; Zhichao Wang

doi:10.1007/978-3-642-25853-4_7

ScienceGate Book Chapters

BOOK-CHAPTER

A Normal Distribution-Based Over-Sampling Approach to Imbalanced Data Classification

Huaxiang Zhang Zhichao Wang

Year: 2011 Lecture notes in computer science Pages: 83-96 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-25853-4_7

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Class (philosophy) Benchmark (surveying) Data set Sampling (signal processing) Data mining Variance (accounting) Set (abstract data type) Artificial intelligence Training set Pattern recognition (psychology) Machine learning

Metrics

Cited By

0.38

FWCI (Field Weighted Citation Impact)

Refs

0.57

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

A Normal Distribution-Based Over-Sampling Approach to Imbalanced Data Classification

Metrics

Citation History

Topics

Related Documents

Denoise-Based Over-Sampling for Imbalanced Data Classification

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

Distribution assessment-based multiple over-sampling with evidence fusion for imbalanced data classification

Borderline over-sampling for imbalanced data classification

Over-sampling algorithm for imbalanced data classification