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 higher-level features from original ones.
Genetic Programming (GP) was usually employed to perform feature construction tasks due …

This paper proposes a novel method called FLGP to construct a classifier device of
capability in feature selection and feature extraction. FLGP is developed with layered …

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 …

As a commonly used technique in data preprocessing for machine learning, feature
selection identifies important features and removes irrelevant, redundant or noise features to …

Feature selection is an important data preprocessing step which is performed before a
learning algorithm is applied. The issue that has to be taken into consideration when …

Feature construction is a pre-processing technique to create new features with better
discriminating ability from the original features. Genetic programming (GP) has been shown …

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

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

Feature selection is a fundamental data preprocessing step in data mining, where its goal is
removing some irrelevant and/or redundant features from a given dataset. In this paper, we …