Name
Abstract | ||
|---|---|---|
Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1504/IJDMB.2015.069416 | International Journal of Data Mining and Bioinformatics |
Keywords | Field | DocType |
ensemble sparse classifier, i(0)-norm solution, feature selection, mass spectrometry, sparse solvers | Biological data,Data mining,Pattern recognition,Feature selection,Computer science,Support vector machine,Bootstrap aggregating,Artificial intelligence,Boosting (machine learning),Data classification,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 2 | 1748-5673 |
Citations | PageRank | References |
2 | 0.44 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunghan Kim | 1 | 16 | 4.26 |
| Fabien Scalzo | 2 | 68 | 15.42 |
| Donatello Telesca | 3 | 2 | 0.44 |
| Xiao Hu | 4 | 72 | 13.64 |