Name
Title | ||
|---|---|---|
Selection of the most relevant terms based on a max-min ratio metric for text classification. |
Abstract | ||
|---|---|---|
•We Illustrated weaknesses of balanced accuracy and normalized difference measures.•We proposed a new feature ranking metric called max-min ratio (MMR).•MMR better estimates the true worth of a term in high class skews.•We tested MMR against 8 well-known metrics on 6 datasets with 2 classifiers.•MMR statistically outperforms metrics in 76% macro F1 cases and 74% micro F1 cases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2018.07.028 | Expert Systems with Applications |
Keywords | Field | DocType |
Text classification,Feature selection,Feature ranking metrics | Data mining,Data set,Normalization (statistics),Feature selection,Pattern recognition,Computer science,Support vector machine,Multiple comparisons problem,Test data,Artificial intelligence,Macro,False positive paradox | Journal |
Volume | ISSN | Citations |
114 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 33 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rehman, A. | 1 | 11 | 1.19 |
| Kashif Javed | 2 | 110 | 8.87 |
| Haroon A. Babri | 3 | 81 | 4.63 |
| Nabeel Asim | 4 | 0 | 0.34 |