ArticleGenomics data2016
A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data.
Article in Genomics data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Predicting student academic achievement using stacked ensemble learning with deep neural networks and fuzzy-based feature selection.Scientific reports · 2025Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.Scientific reports · 2024Article
- Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges.Entropy (Basel, Switzerland) · 2023Article
- A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection.PeerJ. Computer science · 2023Article
- Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review.Artificial intelligence in medicine · 2022Review
- Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.Medical & biological engineering & computing · 2022Article
- Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.Concurrency and computation : practice & experience · 2022Article
- Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.Frontiers in aging neuroscience · 2022Article
- Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.BioMed research international · 2022Article
- Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test.The Malaysian journal of medical sciences : MJMS · 2021Article
- Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System.Entropy (Basel, Switzerland) · 2021Article
- Review
- Stable gene selection by self-representation method in fuzzy sample classification.Medical & biological engineering & computing · 2020Article
- Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients.BMC medical genomics · 2019Article
- A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification.Scientific reports · 2019Article
- Prediction of cancer using customised fuzzy rough machine learning approaches.Healthcare technology letters · 2019Article
- Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy.IET systems biology · 2018Article
- Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.Computational and mathematical methods in medicine · 2018Article
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.
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