| dc.contributor.author | Mwadulo, Mary Walowe | |
| dc.date.accessioned | 2019-09-11T08:46:03Z | |
| dc.date.accessioned | 2020-02-06T14:20:31Z | |
| dc.date.available | 2019-09-11T08:46:03Z | |
| dc.date.available | 2020-02-06T14:20:31Z | |
| dc.date.issued | 2016 | |
| dc.identifier.citation | Mwadulo, M. W. (2016). A review on feature selection methods for classification tasks. International Journal of Computer Applications Technology and Research, 5(6), 395-402. | en_US |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/1007 | |
| dc.description.abstract | In recent years, application of feature selection methods in medical datasets has greatly increased.The challenging task in feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without increasing the complexity of the modeling task.Thus, there is a need to make practitioners aware of feature selection methods that have been successfully applied in medical data sets and highlight future trends in this area.The findings indicate that most existing feature selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal method that achieves the best classification accuracy with fewer features is still an open research area. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Mary | en_US |
| dc.subject | Feature selection, attribute, dimensionality reduction, optimal subset, classification | en_US |
| dc.title | A Review on Feature Selection Methods For Classification Tasks | en_US |
| dc.type | Article | en_US |
