Informative Gene Selection for Microarray Classification via Adaptive Elastic Net with Conditional Mutual Information
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Summary
A new algorithm: Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI) that further improves AEN by incorporating conditional mutual information into the gene selection process and obtains the best classification performance using the least number of genes.
- Type
- preprint
- Published
- 2018-06-05
- Cited by
- 54
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W2805459456
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46937960
Keywords
Elastic net regularization, Gene selection, Regularization (linguistics), Mutual information, Computer science
References
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- Weighted doubly regularized support vector machine and its application to microarray classification with noise
- Hybrid Adaptive Classifier Ensemble
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- Lung Nodule Classification With Multilevel Patch-Based Context Analysis
- A Sparse-Group Lasso
- ν-Nonparallel support vector machine for pattern classification
- A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds
- Addendum: Regularization and variable selection via the elastic net
- Classification and Immunohistochemical Scoring of Breast Tissue Microarray Spots
- Partly adaptive elastic net and its application to microarray classification
- Gene selection using independent variable group analysis for tumor classification
- Detection of Correct and Incorrect Measurements in Real-Time Continuous Glucose Monitoring Systems by Applying a Postprocessing Support Vector Machine
- Sparse group lasso and high dimensional multinomial classification
- Successive Overrelaxation for Laplacian Support Vector Machine
- The Adaptive Lasso and Its Oracle Properties
- Gene-Expression-Based Cancer Subtypes Prediction Through Feature Selection and Transductive SVM
- Simultaneously Optimizing Spatial Spectral Features Based on Mutual Information for EEG Classification
- Selection of relevant genes in cancer diagnosis based on their prediction accuracy
- PATHWISE COORDINATE OPTIMIZATION
Cited by
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- Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data
- Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification.
- A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm
- A memetic algorithm using emperor penguin and social engineering optimization for medical data classification
- A study on metaheuristics approaches for gene selection in microarray data: algorithms, applications and open challenges
- A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets
- Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data
- Combination of Resampling Based Lasso Feature Selection and Ensembles of Regularized Regression Models
- Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.
- A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network
- Sparse Logistic Regression With L1/2 Penalty for Emotion Recognition in Electroencephalography Classification
- Parameters Optimization of Elastic NET for High Dimensional Data using PSO Algorithm
- A New Hybrid and Ensemble Gene Selection Approach with an Enhanced Genetic Algorithm for Classification of Microarray Gene Expression Values on Leukemia Cancer
- SARA: A memetic algorithm for high-dimensional biomedical data
- A Hybrid Feature Selection Optimization Model for High Dimension Data Classification
- Incremental Search for Informative Gene Selection in Cancer Classification
- Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique
- An ensemble soft weighted gene selection-based approach and cancer classification using modified metaheuristic learning
- A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
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