ArticleBioMed research international2017
Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Predicting PIK3CA mutation in breast cancer with machine learning based multimodal image radiomics.European journal of medical research · 2025Article
- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.Scientific reports · 2023Article
- Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data.International journal of molecular sciences · 2022Article
- Article
- Early lung cancer diagnostic biomarker discovery by machine learning methods.Translational oncology · 2021Article
- Robust Feature Selection Approach for Patient Classification using Gene Expression Data.Bioinformation · 2017Article
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4 authors.
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Abstract
The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum
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