Name
Title | ||
|---|---|---|
A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data. |
Abstract | ||
|---|---|---|
The high dimensionality of data is a common problem in classification. In this work, a small number of significant features is investigated to classify data of two sample groups. Various feature selection and classification techniques are applied in a collection of four high-throughput DNA methylation microarray data sets. Using accuracy as a performance metric, the repeated 10-fold cross-validation strategy is implemented to evaluate the different proposed techniques. Combining the Signal to Noise Ratio (SNR) and Wilcoxon rank-sum test filter methods with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) as an embedded method has resulted in a perfect performance. In addition, the linear classifiers showed excellent results compared to others classifiers when applied to such data sets. |
Year | Venue | Keywords |
|---|---|---|
2016 | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2016 | Microarray,DNA Methylation,Feature selection,Classification,Cross-alidation |
DocType | Volume | ISSN |
Conference | 533 | 2194-5357 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alhasan Alkuhlani | 1 | 2 | 0.69 |
| Mohammad Nassef | 2 | 14 | 3.31 |
| Ibrahim Farag | 3 | 20 | 7.01 |