Name
Abstract | ||
|---|---|---|
In recent years, monotonic ordinal classification has increased the focus of attention for machine learning community. Real life problems frequently have monotonicity constraints. Many of the monotonic classifiers require that the input data sets satisfy the monotonicity relationships between its samples. To address this, a conventional strategy consists of relabeling the input data to achieve complete monotonicity. As an alternative, we explore the use of preprocessing algorithms without modifying the class label of the input data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.datak.2017.10.003 | Data & Knowledge Engineering |
Keywords | Field | DocType |
Monotonic classification,Ordinal classification,Training set selection,Data preprocessing,Machine learning | Training set,Monotonic function,Data mining,Data set,Computer science,Ordinal number,Selection algorithm,Preprocessor,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
112 | C | 0169-023X |
Citations | PageRank | References |
3 | 0.43 | 36 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Ramón Cano | 1 | 400 | 15.64 |
| S. G. Garcia | 2 | 569 | 24.88 |