Performance analysis of feature selection methods in software defect prediction: a search method approach
Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …
software systems. The quality of SDP models depends largely on the quality of software …
A multi-objective immune algorithm for intrusion feature selection
Feature selection plays a crucial role in classification problems, which tries to remove
redundant or irrelevant features by mapping high-dimensional data to low-dimensional …
redundant or irrelevant features by mapping high-dimensional data to low-dimensional …
Impact of feature selection methods on the predictive performance of software defect prediction models: an extensive empirical study
Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and
many FS methods have been proposed in the context of software defect prediction (SDP) …
many FS methods have been proposed in the context of software defect prediction (SDP) …
Particle swarm optimization based swarm intelligence for active learning improvement: Application on medical data classification
Semi-supervised learning targets the common situation where labeled data are scarce but
unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In …
unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In …
A novel multimodality based dual fusion integrated approach for efficient and early prediction of glaucoma
As there is currently no exact treatment for glaucoma, early detection and diagnosis are
essential to reduce the risk of this infection. In recent years, Machine learning and deep …
essential to reduce the risk of this infection. In recent years, Machine learning and deep …
A new hybrid system combining active learning and particle swarm optimisation for medical data classification
With the increase of unlabeled data in medical datasets, the labelling process becomes a
more costly task. Therefore, active learning provides a framework to reduce the amount the …
more costly task. Therefore, active learning provides a framework to reduce the amount the …
An IoT based predictive modeling for Glaucoma detection in optical coherence tomography images using hybrid genetic algorithm.
The primary cause of irreversible blindness due to glaucoma is a silent, progressive disease
with no noticeable symptoms. This eye disease gradually and rapidly damages the optic …
with no noticeable symptoms. This eye disease gradually and rapidly damages the optic …
Prediction of stock price movement using an improved NSGA-II-RF algorithm with a three-stage feature engineering process
X Zeng, J Cai, C Liang, C Yuan - Plos one, 2023 - journals.plos.org
Prediction of stock price has been a hot topic in artificial intelligence field. Computational
intelligent methods such as machine learning or deep learning are explored in the …
intelligent methods such as machine learning or deep learning are explored in the …
Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis
Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring
systematic screening in the population over 45 years of age. The diagnosis and …
systematic screening in the population over 45 years of age. The diagnosis and …
Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers
To guarantee that software does not fail, software quality assurance (SQA) teams play a
critical part in the software development procedure. As a result, prioritizing SQA activities is a …
critical part in the software development procedure. As a result, prioritizing SQA activities is a …