0% Complete
Home
/
15th International Conference on Computer and Knowledge Engineering
Adaptive Ensemble Learning for Software Defect Prediction: A Dynamic Weighted Hybrid Model Using SVM, DT, and ANFIS-PSO
Authors :
Mohsen EsfandyariDoulabi
1
Amin Esfandiyari Doulabi
2
Javad Khaligh
3
1- Computer Science Department North Carolina State University Raleigh, USA
2- Computer Science and Engineering Department University of Science and Culture Tehran, Iran
3- Applied Mathematics Department Payame Noor University Tehran, Iran
Keywords :
Software Quality،Defect Prediction،Ensemble Learning،ANFIS-PSO
Abstract :
Software quality prediction is vital for reducing maintenance costs and improving system reliability. However, traditional machine learning models and fixed-weight ensembles often exhibit limited adaptability on diverse and imbalanced software defect datasets. This study introduces a Dynamic Weighted Hybrid Model (DWHM) that overcomes these limitations by integrating Support Vector Machines (SVM), Decision Trees (DT), and an Adaptive Neuro-Fuzzy Inference System optimized with Particle Swarm Optimization (ANFIS-PSO). Unlike static approaches, the DWHM employs a dynamic weighting mechanism that adaptively assigns weights to each base learner based on local performance metrics, thereby enhancing predictive robustness and generalization. Experiments on NASA MDP and PROMISE datasets demonstrate that DWHM consistently outperforms individual classifiers and fixed-weight ensembles, achieving an average accuracy of 90.2% and a Matthews Correlation Coefficient (MCC) of 0.79. These statistically significant results highlight the model's ability to capture complex relationships and provide a reliable, interpretable framework for more effective resource allocation in software quality assurance.
Papers List
List of archived papers
LightFedSelect: A Lightweight Framework for Byzantine-Robust Federated Learning
Seyed Saeed Razavi - Seyed Arsalan Vasegh Rahim Parvar - Soroosh Dadashi Pakdeh - Mohammad Matin Rezaeifard - Morteza Mollaie Chafi - Reza Ebrahimi Atani
Time Series Analysis by Bi-GRU for Forecasting Bitcoin Trends based on Sentiment Analysis
Fatemeh Saadatmand - Mohammad Ali Zare Chahoki
Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration
Dara Rahmati - Mohammad Hadi Foroughi - Ali Bagherzadeh - Mehdi Foroughi - Saeid Gorgin
Intensity-Image Reconstruction Using Event Camera Data by Changing in LSTM Update
Arezoo Rahmati Soltangholi - Ahad Harati - Abedin Vahedian
Hybrid Flow-Rule Placement Method of Proactive and Reactive in SDNs
Mohammadreza Khoobbakht - Mohammadreza Noei - Mohammadreza Parvizimosaed
Weakly Supervised Convolutional Neural Network for Automatic Gleason Grading of Prostate Cancer
Maryam Kamareh - Mohammad Sadegh Helfroush - Kamran Kazemi
Enhanced Principal-curve based Classifiers for Time-series Label Prediction
Seyed Aref Hakimzadeh - Koorush Ziarati
Graph Attention Networks for Modeling Multi-Sensor Relationships in Early Prediction of Critical Events in ICU Patients
Amir Akhavan Saffar - Danial Eskandari Faruji - Javad Hamidzadeh
Fast and Accurate Motif Discovery in Protein Sequences Using Parallel Processing with OpenMP
Rahele Mohammadi - Mahmoud Naghibzadeh - Abdorreza Savadi
Enhancing Persian Word Sense Disambiguation with Large Language Models: Techniques and Applications
Fatemeh Zahra Arshia - Saeedeh Sadat Sadidpour
more
Samin Hamayesh - Version 44.5.0