Advancements in medical technology have created numerous large datasets including
many features. Usually, all captured features are not necessary, and there are redundant …

In this work, an enhanced moth flame optimization (MFO) algorithm is proposed as a search
strategy within a wrapper feature selection (FS) framework. It aims mainly to improve the …

Feature selection methods are used to select a subset of features from data, therefore only
the useful information can be mined from the samples to get better accuracy and improves …

Feature selection is an NP-hard problem to remove irrelevant and redundant features with
no predictive information to increase the performance of machine learning algorithms. Many …

A well-constructed classification model highly depends on input feature subsets from a
dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be …

Feature selection commonly refers to a process of using the candidate algorithm to detect
the optimal feature sets during the preprocessing steps in machine learning and data …

This paper provides an alternative method for creating an optimal subset from features
which in turn represent the whole features through improving the moth-flame optimization …

Feature selection (FS) is a classic and challenging optimization task in the field of machine
learning and data mining. Gradient-based optimizer (GBO) is a recently developed …

Accurate classification of high-dimensional biomedical data highly depends on the efficient
recognition of the data's main features which can be used to assist diagnose related …

Feature selection (FS) is used to solve hard optimization problems in artificial intelligence
and data mining. In the FS process, some, rather than all of the features of a dataset are …