A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges
Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …
two primary objectives in feature selection, which is inherently a multiobjective task …
Information-theory-based nondominated sorting ant colony optimization for multiobjective feature selection in classification
Feature selection (FS) has received significant attention since the use of a well-selected
subset of features may achieve better classification performance than that of full features in …
subset of features may achieve better classification performance than that of full features in …
Feature selection using diversity-based multi-objective binary differential evolution
By identifying relevant features from the original data, feature selection methods can
maintain or improve the classification accuracy and reduce the dimensionality. Recently …
maintain or improve the classification accuracy and reduce the dimensionality. Recently …
Evolutionary multitasking descriptor optimization for point cloud registration
Point cloud registration is an important task for other point cloud tasks. Feature-based
methods are widely adopted for their speed and efficiency in point cloud registration. The …
methods are widely adopted for their speed and efficiency in point cloud registration. The …
A bidirectional dynamic grou** multi-objective evolutionary algorithm for feature selection on high-dimensional classification
As a key preprocessing step in classification, feature selection involves two conflicting
objectives: maximizing the classification accuracy and minimizing the number of selected …
objectives: maximizing the classification accuracy and minimizing the number of selected …
Solving multi-objective feature selection problems in classification via problem reformulation and duplication handling
Reducing the number of selected features and improving the classification performance are
two major objectives in feature selection, which can be viewed as a multi-objective …
two major objectives in feature selection, which can be viewed as a multi-objective …
Feature clustering-Assisted feature selection with differential evolution
Modern data collection technologies may produce thousands of or even more features in a
single dataset. The high dimensionality of data poses a barrier to determining discriminating …
single dataset. The high dimensionality of data poses a barrier to determining discriminating …
Region-aware hierarchical latent feature representation learning-guided clustering for hyperspectral band selection
Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral
images (HSIs). For most existing clustering-based band selection methods, they directly …
images (HSIs). For most existing clustering-based band selection methods, they directly …
Benefiting from single-objective feature selection to multiobjective feature selection: a multiform approach
Evolutionary multiobjective feature selection (FS) has gained increasing attention in recent
years. However, it still faces some challenges, for example, the frequently appeared …
years. However, it still faces some challenges, for example, the frequently appeared …
Locating multiple equivalent feature subsets in feature selection for imbalanced classification
Feature selection can be used to solve imbalanced classification problems encountered in
big data projects. There often exist multiple feature subsets achieving the same accuracy …
big data projects. There often exist multiple feature subsets achieving the same accuracy …