X. Deng and Y. Xu, "Cancer Classification Using Microarray Data By DPCAForest," 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 1081-1087, doi: 10.1109/ICTAI.2019.00151.

Release time:2024-03-13

Journal:2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)

Abstract:Supervised learning is a powerful tool that has shown promise when applied towards bioinformatics data sets. Deep forest, a supervised ensemble model based on decision trees, has been proven to have excellent classification performance and strong generalization ability across different fields. However, when dealing with high-dimensional and small-sample gene expression data, commonly used supervised learning methods including deep forest may not be effective. In this paper, we propose DPCAForest, a deep-forest-based model, which inte- grates deep forest and dynamic principle component analysis. DPCAForest adaptively generates the minority samples based on sample distribution, then conducts principle component analysis dynamically synchronized with growth of deep forest to reveal the important features with the highest variance. Dynamic PCA enables the model to perform feature extraction in a data- driven way based on cross-validation, and the model can obtain fusion information across layers. In the experimental studies, DPCAForest is verified on Adenocarcinoma, Brain, Colon, Small Round Blue Cell Tumors (SRBCTs) and NCI-60 cancer data sets and demonstrated desirable or better performance than state-of- the-art methods in terms of accuracy and F-Measure.

Note:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49268.htm

Translation or Not:no