A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining
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Summary
A novel filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization, named orthogonal MIC Feature Selection (OMICFS), was proposed to solve the problem of irrelevant redundancy in the classical filter method minimal-Redundancy-Maximal-Relevance.
- Type
- article
- Published
- 2017-10-01
- Cited by
- 78
- References
- 30
- OpenAlex
- https://openalex.org/W2747166117
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11214800
Keywords
Orthogonalization, Redundancy (engineering), Feature selection, Minimum redundancy feature selection, Pattern recognition (psychology)
References
- Applied Predictive Modeling
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- Big data in biomedicine
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- Using covariates for improving the minimum redundancy maximum relevance feature selection method
- Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
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- A review of feature selection techniques in bioinformatics
- Radiomics: Extracting more information from medical images using advanced feature analysis
- Cyberinfrastructure: Empowering a "Third Way" in Biomedical Research
- Optimization of ECG Classification by Means of Feature Selection
- Effective global approaches for mutual information based feature selection
- Feature selection and transduction for prediction of molecular bioactivity for drug design
- minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers
- LIBSVM: A library for support vector machines
- Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
- Discussion of “Sure Independence Screening for Ultra-High Dimensional Feature Space
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- Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression
- A Review of Data Mining Methods in Bioinformatics
- Joint neighborhood entropy-based gene selection method with fisher score for tumor classification
- Feature Selection With Ensemble Learning Based on Improved Dempster-Shafer Evidence Fusion
- Frequency based feature selection method using whale algorithm.
- A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures
- Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics Data
- Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology
- A data-driven M2 approach for evidential network structure learning
- Heuristic filter feature selection methods for medical datasets.
- Identification of potential biomarkers on microarray data using distributed gene selection approach.
- Fusion Feature Selection: New Insights into Feature Subset Detection in Biological Data Mining
- An efficient wavelength selection method based on the maximal information coefficient for multivariate spectral calibration
- BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics.
- Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees
- High-Precision Power Load Forecasting Using Real-time Temperature Information and Deep Learning Method
- Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks
- Ensamble Based Multi Filters Algorithm for Tumor Classification in High Dimensional Microarray Dataset
- Probabilistic Load Forecasting with High Penetration of Renewable Energy Based on Variable Selection and Residual Modeling
- Deep learning approach for microarray cancer data classification
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