Analysis and comparison of feature selection methods towards performance and stability
The amount of gathered data is increasing at unprecedented rates for machine learning
applications such as natural language processing, computer vision, and bioinformatics. This …
applications such as natural language processing, computer vision, and bioinformatics. This …
The use of gene expression datasets in feature selection research: 20 years of inherent bias?
Feature selection algorithms are frequently employed in preprocessing machine learning
pipelines applied to biological data to identify relevant features. The use of feature selection …
pipelines applied to biological data to identify relevant features. The use of feature selection …
Development of symbolic expressions ensemble for breast cancer type classification using genetic programming symbolic classifier and decision tree classifier
N Anđelić, S Baressi Šegota - Cancers, 2023 - mdpi.com
Simple Summary Breast cancer is a type of cancer with several sub-types and correct sub-
type classification based on a large number of gene expressions is challenging even for …
type classification based on a large number of gene expressions is challenging even for …
A hybrid machine learning approach to screen optimal predictors for the classification of primary breast tumors from gene expression microarray data
The high dimensionality and sparsity of the microarray gene expression data make it
challenging to analyze and screen the optimal subset of genes as predictors of breast …
challenging to analyze and screen the optimal subset of genes as predictors of breast …
Exponentially convergent algorithms for supervised matrix factorization
Supervised matrix factorization (SMF) is a classical machine learning method that
simultaneously seeks feature extraction and classification tasks, which are not necessarily a …
simultaneously seeks feature extraction and classification tasks, which are not necessarily a …
Linear programming based computational technique for leukemia classification using gene expression profile
M Ilyas, KM Aamir, S Manzoor, M Deriche - Plos one, 2023 - journals.plos.org
Cancer is a serious public health concern worldwide and is the leading cause of death.
Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer …
Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer …
Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data
The lack of interpretability of neural networks is partially why they are not adopted in a wider
variety of applications. Many works focus on explaining their predictions, but few take tabular …
variety of applications. Many works focus on explaining their predictions, but few take tabular …
Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets
The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted
human health and the economy, especially in countries struggling with financial resources …
human health and the economy, especially in countries struggling with financial resources …
Multi-approach bioinformatics analysis of curated omics data provides a gene expression panorama for multiple cancer types
Studies describing the expression patterns and biomarkers for the tumoral process increase
in number every year. The availability of new datasets, although essential, also creates a …
in number every year. The availability of new datasets, although essential, also creates a …
Snekhorn: Dimension reduction with symmetric entropic affinities
Many approaches in machine learning rely on a weighted graph to encode thesimilarities
between samples in a dataset. Entropic affinities (EAs), which are notably used in the …
between samples in a dataset. Entropic affinities (EAs), which are notably used in the …