Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems
Searching for the optimal subset of features is known as a challenging problem in feature
selection process. To deal with the difficulties involved in this problem, a robust and reliable …
selection process. To deal with the difficulties involved in this problem, a robust and reliable …
Boolean Particle Swarm Optimization with various Evolutionary Population Dynamics approaches for feature selection problems
In the feature selection process, reaching the best subset of features is considered a difficult
task. To deal with the complexity associated with this problem, a sophisticated and robust …
task. To deal with the complexity associated with this problem, a sophisticated and robust …
[HTML][HTML] Clustering mixed numerical and categorical data with missing values
This paper proposes a novel framework for clustering mixed numerical and categorical data
with missing values. It integrates the imputation and clustering steps into a single process …
with missing values. It integrates the imputation and clustering steps into a single process …
Partial multi-dividing ontology learning algorithm
As an effective data representation, storage, management, calculation and model for
analysis, ontology has attracted more and more attention by researchers and it has been …
analysis, ontology has attracted more and more attention by researchers and it has been …
Intelligent skin cancer detection using enhanced particle swarm optimization
In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic
images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature …
images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature …
Discriminative subspace matrix factorization for multiview data clustering
In a real-world scenario, an object is easily considered as features combined by multiple
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
views in reality. Thus, multiview features can be encoded into a unified and discriminative …
Deep low-rank subspace ensemble for multi-view clustering
Multi-view clustering aims to incorporate complementary information from different data
views for more effective clustering. However, it is difficult to obtain the true categories of data …
views for more effective clustering. However, it is difficult to obtain the true categories of data …
Semi-supervised feature selection via adaptive structure learning and constrained graph learning
Graph-based sparse feature selection plays an important role in semi-supervised feature
selection, which greatly improves the performance of feature selection. However, most …
selection, which greatly improves the performance of feature selection. However, most …
Robust unsupervised feature selection via dual self-representation and manifold regularization
Unsupervised feature selection has become an important and challenging pre-processing
step in machine learning and data mining since large amount of unlabelled high …
step in machine learning and data mining since large amount of unlabelled high …
Non-negative spectral learning and sparse regression-based dual-graph regularized feature selection
Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …
data. In recent years, many feature selection algorithms have been proposed, but most of …