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| Content Provider | Springer Nature Link |
|---|---|
| Author | Almuhaideb, Sarab Menai, Mohamed El Bachir |
| Copyright Year | 2016 |
| Abstract | The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics ofmedical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations considered include the discretization of numeric attributes, the selection of attribute subset(s), and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained. |
| Starting Page | 1082 |
| Ending Page | 1102 |
| Page Count | 21 |
| File Format | |
| ISSN | 20952228 |
| Journal | Frontiers of Computer Science in China |
| Volume Number | 10 |
| Issue Number | 6 |
| e-ISSN | 20952236 |
| Language | English |
| Publisher | Higher Education Press |
| Publisher Date | 2016-10-04 |
| Publisher Institution | Chinese Universities |
| Publisher Place | Beijing |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | classification ant colony optimization medical data classification preprocessing feature subset selection discretization Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Science |
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