Diversity based imbalance learning approach for software fault prediction using machine learning models

Pravali Manchala; Manjubala Bisi

doi:10.1016/j.asoc.2022.109069

ScienceGate Book Chapters

JOURNAL ARTICLE

Diversity based imbalance learning approach for software fault prediction using machine learning models

Pravali Manchala Manjubala Bisi

Year: 2022 Journal: Applied Soft Computing Vol: 124 Pages: 109069-109069 Publisher: Elsevier BV

DOI: 10.1016/j.asoc.2022.109069

Get Full-Text PDF Get Analytical Report

Keywords:

Oversampling Computer science Artificial intelligence Support vector machine Machine learning Artificial neural network Wilcoxon signed-rank test Data mining Decision tree Naive Bayes classifier Centroid Pattern recognition (psychology) Statistics Mathematics

Metrics

Cited By

17.10

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Software Engineering Research

Physical Sciences → Computer Science → Information Systems

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Software Reliability and Analysis Research

Physical Sciences → Computer Science → Software

Diversity based imbalance learning approach for software fault prediction using machine learning models

Metrics

Citation History

Topics

Related Documents

Software Fault Prediction Using Machine Learning Models

Machine learning based Software Fault Prediction models

Software Fault Prediction using K-Mean-Based Machine Learning Approach

Software Fault Prediction Using Machine Learning Models and Comparative Analysis

Software Fault Prediction Using Machine Learning Algorithms