Investigation of rotation forest method applied to property price prediction

Authors:
Tadeusz Lasota;Tomasz Łuczak;Bogdan Trawiński
Affiliations:
Dept. of Spatial Management, Wrocław University of Environmental and Life Sciences, Wrocław, Poland;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

Citing 16
Cited 0

Bagging predictors

Machine Learning
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Forests

Machine Learning
Random decision forests

ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Rotation Forest: A New Classifier Ensemble Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Theoretical Analysis of Bagging as a Linear Combination of Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal

International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA

ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Comparison of data driven models for the valuation of residential premises using KEEL

International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Analysis of bagging ensembles of fuzzy models for premises valuation

ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Combining bagging, boosting, rotation forest and random subspace methods

Artificial Intelligence Review
Investigation of bagging ensembles of genetic neural networks and fuzzy systems for real estate appraisal

ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Investigation of random subspace and random forest methods applied to property valuation data

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
On employing fuzzy modeling algorithms for the valuation of residential premises

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

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.