Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Fatihah Mohd; Masita Abdul Jalil; Noor Maizura Mohamad Noor; Suryani Ismail; Wan Fatin Fatihah Yahya; Mumtazimah Mohamad

doi:10.1007/978-3-030-36365-9_8

ScienceGate Book Chapters

BOOK-CHAPTER

Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Fatihah Mohd Masita Abdul Jalil Noor Maizura Mohamad Noor Suryani Ismail Wan Fatin Fatihah Yahya Mumtazimah Mohamad

Year: 2019 Communications in computer and information science Pages: 99-110 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-36365-9_8

Get Full-Text PDF Get Analytical Report

Keywords:

Oversampling Resampling Computer science Artificial intelligence Class (philosophy) Sampling (signal processing) Machine learning Pattern recognition (psychology) Data mining

Metrics

Cited By

0.53

FWCI (Field Weighted Citation Impact)

Refs

0.70

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Electricity Theft Detection Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Artificial Intelligence in Healthcare

Health Sciences → Health Professions → Health Information Management

Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Metrics

Citation History

Topics

Related Documents

Imbalanced data classification using improved synthetic minority over-sampling technique

Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE)

Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced data in poverty classification

Multi-Class Imbalanced Data Classification Using TwinSVM-One versus All and Synthetic Minority Over-sampling Technique

An Analysis Of Classification Of Imbalanced Datasets By Using Synthetic Minority Over-Sampling Technique