.png){kind=icon}
Return
An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3123090.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe), the proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.
Keywords:
Feature extraction
Cancer
Support vector machines
Classification algorithms
Task analysis
Biological system modeling
Mutual information
Cancer classification
feature selection
genomic microarray data
support vector machine
ensemble minimum redundancy--maximum relevance
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
Microarray Analysis of Early Stage Serous Ovarian Cancers Shows Profiles Predictive of Favorable Outcome.png)
CLINICAL CANCER RESEARCH
OAAI
CLINICAL CANCER RESEARCHIF10.2
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more