Feature selection methods on gene expression microarray data for cancer classification: A systematic review
This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …
processing microarray data with comprehensive information about the main research …
Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: A comprehensive review
Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …
A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in …
G Yao, X Hu, G Wang - Expert Systems with Applications, 2022 - Elsevier
Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …
making and early credit crisis warnings. Improving the prediction performance of this task is …
On developing an automatic threshold applied to feature selection ensembles
Feature selection ensemble methods are a recent approach aiming at adding diversity in
sets of selected features, improving performance and obtaining more robust and stable …
sets of selected features, improving performance and obtaining more robust and stable …
General feature selection for failure prediction in large-scale SSD deployment
Solid-state drive (SSD) failures are likely to cause system-level failures leading to downtime,
enabling SSD failure prediction to be critical to large-scale SSD deployment. Existing SSD …
enabling SSD failure prediction to be critical to large-scale SSD deployment. Existing SSD …
A general framework for class label specific mutual information feature selection method
Information theory-based feature selection (ITFS) methods select a single subset of features
for all classes based on the following criteria: 1) minimizing redundancy between the …
for all classes based on the following criteria: 1) minimizing redundancy between the …
[图书][B] Recent advances in ensembles for feature selection
V Bolón-Canedo, A Alonso-Betanzos - 2018 - Springer
Classically, machine learning methods have used a single learning model to solve a given
problem. However, the technique of using multiple prediction models for solving the same …
problem. However, the technique of using multiple prediction models for solving the same …
Ensemble feature ranking for cost-based non-overlapping groups: A case study of chronic kidney disease diagnosis in developing countries
Chronic kidney disease (CKD) is one of the leading medical ailments in developing
countries. Due to the limited healthcare infrastructure and the lack of trained human …
countries. Due to the limited healthcare infrastructure and the lack of trained human …
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
Background Feature selection is often used to identify the important features in a dataset but
can produce unstable results when applied to high-dimensional data. The stability of feature …
can produce unstable results when applied to high-dimensional data. The stability of feature …
Cost-sensitive ensemble feature ranking and automatic threshold selection for chronic kidney disease diagnosis
Automated medical diagnosis is one of the important machine learning applications in the
domain of healthcare. In this regard, most of the approaches primarily focus on optimizing …
domain of healthcare. In this regard, most of the approaches primarily focus on optimizing …