A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

By Zhenyu Chen, Jianping Li, Liwei Wei. 2007

124 citations recorded by the source corpus.

Subfields: Cancer Research, Information Systems, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Andrew P. Bradley
2. Nahla H. Barakat

In Information Systems

1. Salvador Garcia
2. Francisco Herrera
3. Andrew P. Bradley
4. Nahla H. Barakat

In Molecular Biology

1. Francisco Herrera
2. Andrew P. Bradley
3. Nahla H. Barakat

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “In a similar study conducted in [245] , extracting rules for gene expression data, the authors presented a novel technique as a component of a multi-kernel SVM.”
• Human-in-the-loop machine learning: a state of the art (2022, methodology): “For SVMs, post-hoc explainability techniques applied are explanation by simplification, local explanations, visualizations and explanations by example (Barakat and Bradley 2007; Chen et al. 2007; Gaonkar et al. 2015).”
• Rule extraction from support vector machines: A review (2010, methodology): “Fidelity1⁄4 No: of patterns where the cla Total The extracted rules by this method have good comprehensibility, good generalization performance while compact gene subsets were found [22].”