Background: Informative gene selection is an essential step in performing tumor classification.
However, it is difficult to select informative genes related to tumors from large-scale gene
expression profiles because of their characteristics, such as high dimensionality, relatively small
samples, and class imbalance, and some genes are superfluous and irrelevant.
Objective: Many researchers analyze and process gene expression data to obtain classified gene
subsets by using machine learning methods. However, the gene expression profiles of tumors often
have massive computational challenges. In addition, when improving feature importance and classification
accuracy, cost estimation is often ignored in traditional feature selection algorithms,
which makes tumor classification more difficult.
Methods: In this study, a novel informative gene selection method based on cost-sensitive fast correlation-
based filter (CS-FCBF) feature selection is proposed.
Results: First, the symmetric uncertainty index is used to evaluate the correlation between informative
genes and class labels, then a large number of irrelevant and redundant genes are quickly
filtered according to importance. Thereby, a candidate gene subset is generated. Second, costsensitive
learning, which introduces the misclassification cost matrix and support vector machine
attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification
loss. Finally, the candidate gene subset is optimized.
Conclusion: This experiment was verified in eight independent tumor datasets. By comparing and
analyzing CS-FCBF with another three hybrids of typical gene selection algorithms combined with
cost-sensitive learning, we found that the method proposed in this study has a better classification
performance with fewer selected genes, which might provide guidance in tumor diagnosis and research.