Selecting Predictor Variables in Two-Group Classification Problems
Bibliographic Data
| ID | 20286383 |
|---|---|
| Authors | John D Morris (Florida Atlantic University), Alice Meshbane (Florida Atlantic University) |
| Year | 1995 |
| Volume | 55 |
| Issue | 3 |
| Pages | 438-441 |
| Publication date | 1995-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164495055003008 |
| OpenAlex | W2088469176 |
| Language | EN |
| References cited | 10 |
One approach to the problem of selecting predictor variables in the two-group classification situation is to compare the cross-validated classification accuracies of all possible subsets. Because of the obvious time constraints of this approach for even a moderate number of predictors, a computer program was written to accomplish the task. The program examines the cross-validated classification hit rates of all possible subsets of predictor variables and identifies the user-specified number of best subsets overall and for each subset size
Group (periodic table) · Machine learning · Statistics · Task (project management) · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Mathematics
| Highly cited | No |
|---|