The purpose of feature construction is to create new higher-level features from original ones.
Genetic Programming (GP) was usually employed to perform feature construction tasks due …

Feature construction and feature selection are two common pre-processing methods for
classification. Genetic Programming (GP) can be used to solve feature construction and …

The purpose of feature construction is to create new high level features from the original
features. When Genetic Programming (GP) is applied to wrapper-based feature construction …

Genetic Programming (GP) has been successfully applied to image classification and
achieved promising results. However, most existing methods either address binary image …

Feature manipulation refers to the process by which the input space of a machine learning
task is altered in order to improve the learning quality and performance. Three major aspects …

Feature selection has always been a critical step in pattern recognition, in which
evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used …

Feature selection is an important data preprocessing step in machine learning and data
mining, such as classification tasks. Research on feature selection has been extensively …

Feature selection is a data processing method which aims to select effective feature subsets
from original features. Feature selection based on evolutionary computation (EC) algorithms …

Data representation is an important factor in deciding the performance of machine learning
algorithms including classification. Feature construction (FC) can combine original features …

The quality of the search space is an important factor that influences the performance of any
machine learning algorithm including its classification. The attributes that define the search …