Name
Abstract | ||
|---|---|---|
A few years ago a new classifier ensemble method, called rotation forest, was devised. The technique applies Principal Component Analysis to rotate the original feature axes in order to obtain different training sets for learning base classifiers. In the paper we report the results of the investigation aimed to compare the predictive performance of rotation forest with random forest models, bagging ensembles and single models using two popular algorithms M5 tree and multilayer perceptron. All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout splits. A real-world dataset of sales/purchase transactions derived from a cadastral system served as basis for benchmarking the methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29347-4_47 | ICAISC (1) |
Keywords | Field | DocType |
random forest model,m5 tree,base classifier,weka data mining system,different training set,principal component analysis,10-fold cross-validation,rotation forest method,property price prediction,cadastral system,bagging ensemble,rotation forest,random forest,bagging | Data mining,Rotation,Cadastre,Computer science,Multilayer perceptron,Artificial intelligence,Rotation forest,Random forest,Classifier (linguistics),Machine learning,Principal component analysis,Benchmarking | Conference |
Volume | ISSN | Citations |
7267 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tadeusz Lasota | 1 | 348 | 25.33 |
| Tomasz Łuczak | 2 | 225 | 40.26 |
| Bogdan Trawiński | 3 | 288 | 24.72 |