An Improved SVM-RFE Based on $F$ -Statistic and mPDC for Gene Selection in Cancer Classification

et al. 2021

Preprint

Background Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task, and the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results The proposed Method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the results on the cleft lip and palate data set with known biomarkers provided by the cooperative hospital show that compared with other methods, our method can preferentially select these biomarkers. Method In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. In this method, the features are first clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR, and the final result is output according to the cumulative weight reordering. Conclusion The proposed method can effectively remove redundant features. The algorithm's output has high stability and classification accuracy and can potentially select potential biomarkers.

Section: Fundingmentioning

confidence: 99%

ILRC : A Hybrid Biomarker Discovery Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

Xie

et al. 2021

Preprint

“…Requests for access to these data should be made to the corresponding author Author details 1 Biomedical and Information Engineering School, Northeastern University, China. 2 School of Computer Science and Engineering, Northeastern University, China. 3 Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Ministry of Education, China.…”

Section: Fundingmentioning

confidence: 99%

“…Microarray data is a valuable tool for analyzing gene expression profiles [1]. This kind of data usually contains a small number of biological or clinical samples and a large number of genes (features) that are not related to the target disease [2]. In addition, microarray data shows a high complexity, i.e., genes are direct-or inter-related, which results in a high degree of redundancy.…”

Section: Introductionmentioning

confidence: 99%

ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

Xie

et al. 2021

Preprint

Background: Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task, and the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results: The proposed Mehtod has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the results on the cleft lip and palate data set with known biomarkers provided by the cooperative hospital show that compared with other methods, our method can preferentially select these biomarkers. Method: In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. In this method, the features are first clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR, and the final result is output according to the cumulative weight reordering. Conclusion: The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy and can potentially select potential biomarkers.

“…Another default assumption of many classification algorithms is the consistency of misclassification costs, which are rarely met in the real world. Usually, positive samples with fewer instances are the target class of interest to users (for example, fraudulent transaction detection [3], fault diagnosis of mechanical system [4], rare disease diagnosis [5]- [7]).…”

Section: Introductionmentioning

confidence: 99%

Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

2021

IEEE Access

Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It's a challenge for standard algorithms designed for balanced class distributions. Although there are various strategies to solve this problem, generating artificial data to achieve a relatively balanced class distribution is universal rather than directly modifying specific classification algorithms. The oversampled data can be combined with any userspecified algorithm without any restrictions. In this paper, we present a novel oversampling method, Global Data Distribution Weighted Synthetic Oversampling Technique (GDDSYN). By applying clustering, optimizing the selection criteria of the minority class samples that are used to generate synthetic samples, avoiding generating more noise samples. GDDSYN assigns weights for the number of synthetic samples to tackle the within-class imbalance and between-class imbalance simultaneously, according to the informative level of the sample and the sparsity of the cluster to which the sample belongs. The use of scores with Silhouette Coefficient and Mutual Information helps the k-means algorithm set a reasonable number of clusters for the minority and majority classes respectively so that the clustering effect can be guaranteed. Next, by using clustering information, synthetic samples' generation path is improved to avoid class overlap. Additionally, GDDSYN has been evaluated extensively on 10 artificial and 10 real-world data sets. The empirical results show that our method is outperforms or comparable with some other existing methods in terms of assessment metrics when artificial data generated by GDDSYN are used.