Name
Title | ||
|---|---|---|
Evolving data sets to highlight the performance differences between machine learning classifiers |
Abstract | ||
|---|---|---|
We present a preliminary study to evolve data sets that maximize performance differences between multiple machine learning classifiers. The aim is to provide useful information towards the decision of which machine learning classifier to use given a particular data set. While literature already exists on comparing multiple classifiers across multiple pre-existing data sets, our approach is novel and unique in that we evolved completely new data sets designed to highlight the performance differences between supervised learning classifiers. By investigating these evolved data sets, we hope to add to the knowledge base concerning which classifiers are appropriate for specific real world classification tasks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330784.2330907 | GECCO (Companion) |
Keywords | Field | DocType |
multiple pre-existing data set,multiple machine,knowledge base,performance difference,evolving data,particular data,supervised learning classifier,new data,multiple classifier,preliminary study,supervised learning,evolutionary computation,evolutionary computing,machine learning | Online machine learning,Semi-supervised learning,Active learning (machine learning),Computer science,Random subspace method,Cascading classifiers,Supervised learning,Artificial intelligence,Linear classifier,Machine learning,Learning classifier system | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Raway | 1 | 1 | 1.08 |
| David J. Schaffer | 2 | 0 | 0.34 |
| Kenneth J. Kurtz | 3 | 55 | 18.00 |
| Hiroki Sayama | 4 | 319 | 49.14 |