Name
Abstract | ||
|---|---|---|
In supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10462-010-9165-y | Artif. Intell. Rev. |
Keywords | Field | DocType |
Instance selection,Supervised learning,Data reduction,Pre-processing | Training set,Data mining,Instance-based learning,Computer science,Supervised learning,Instance selection,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 2 | 0269-2821 |
Citations | PageRank | References |
104 | 2.49 | 42 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Arturo Olvera-López | 1 | 120 | 5.61 |
| J. Ariel Carrasco-Ochoa | 2 | 233 | 12.86 |
| J. Francisco Martínez-Trinidad | 3 | 122 | 6.66 |
| J. Kittler | 4 | 14346 | 1465.03 |