Article

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

2022-03-14

Abstract excerpt

<title>Abstract</title> <p>Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adver...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8b5c6140-2835-5e11-a80f-33eb82375d40
DOI: 10.21203/rs.3.rs-1402946/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray DataDOI 10.21203/rs.3.rs-1402946/v1Read

Binary Puma Optimizer: A Metaheuristic Approach for Gene Selection in Bioinformatics with Machine Learning ClassificationSemantic proximity Read

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.Semantic proximity Read

Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression DataSemantic proximity Read

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.Semantic proximity Read

A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer DetectionSemantic proximity Read

Hybrid Metaheuristic Feature Selection for Enhanced Breast Cancer Detection in Digital Mammography: A Radiomics and Deep Learning Approach with Cross-Dataset ValidationSemantic proximity Read

An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasetsSemantic proximity Read

A Novel Framework to Classify Cancer through a Consecutive Utilization of Hybrid Feature Selection and Deep LearningSemantic proximity Read

A hybrid BPSO-CGA approach for gene selection and classification of microarray data.Semantic proximity Read

Genes expression Dimensions reduction for Cancer classification based on optimized deep neural network with Giza pyramid constructionSemantic proximity Read

Mutation-based Binary Aquila optimizer for gene selection in cancer classification.Semantic proximity Read

Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning MachinesSemantic proximity Read