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A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

https://doi.org/10.1109/access.2021.3075942
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Classifying Microarray Data Using Support Vector Machines
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Gene selection from microarray data for cancer classification—a machine learning approach
inferredcites →https://doi.org/10.1016/j.compbiolchem.2015.03.001openalex-snapshot
Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification
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inferredcites →https://doi.org/10.1016/j.engappai.2019.103330openalex-snapshot
Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems
inferredcites →https://doi.org/10.1016/j.engappai.2020.103541openalex-snapshot
Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization
inferredcites →https://doi.org/10.1016/j.gpb.2017.08.002openalex-snapshot
Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification
inferredcites →https://doi.org/10.1016/j.ins.2014.05.042openalex-snapshot
A review of microarray datasets and applied feature selection methods
inferredcites →https://doi.org/10.1016/j.ins.2019.01.062openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.neucom.2020.04.013openalex-snapshot
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Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1038/35000501openalex-snapshot
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
inferredcites →https://doi.org/10.1038/89044openalex-snapshot
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
inferredcites →https://doi.org/10.1038/ng765openalex-snapshot
MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia
inferredcites →https://doi.org/10.1109/access.2020.3024095openalex-snapshot
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer
inferredcites →https://doi.org/10.1109/cec.2007.4424483openalex-snapshot
Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms
inferredcites →https://doi.org/10.1109/icnn.1995.488968openalex-snapshot
Particle swarm optimization
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A Branch and Bound Algorithm for Feature Subset Selection
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Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems With Application of an Ironmaking Blast Furnace
inferredcites →https://doi.org/10.1126/science.286.5439.531openalex-snapshot
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
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Machine learning and data mining
inferredcites →https://doi.org/10.13187/mai.2015.6.171openalex-snapshot
Data-Mining Techniques to Classify Microarray Gene Expression Data Using Gene Selection by SVD and Information Gain
inferredcites →https://doi.org/10.18178/ijmlc.2016.6.3.596openalex-snapshot
ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification
inferredcites →https://doi.org/10.4137/cin.s2655openalex-snapshot
Microarray-Based Cancer Prediction Using Soft Computing Approach
inferredcites →https://doi.org/10.7763/ijbbb.2014.v4.332openalex-snapshot
The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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