@inproceedings{355c33f2b9c84bec849699f405fdf8d2,
title = "Random projection ensemble classifiers",
abstract = "We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset's geometrical structure to a given distortion. This reduces the computational complexity of the model construction as well as the complexity of the classification. Furthermore, dimensionality reduction removes noisy features from the data and also represents the information which is inherent in the raw data by using a small number of features. The noise removal increases the accuracy of the classifier. The proposed scheme was tested using WEKA based procedures that were applied to 16 benchmark dataset from the UCI repository.",
keywords = "Classification, Ensemble methods, Pattern recognition, Random projections",
author = "Alon Schclar and Lior Rokach",
year = "2009",
month = jan,
day = "1",
doi = "10.1007/978-3-642-01347-8\_26",
language = "English",
isbn = "9783642013461",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "309--316",
booktitle = "Enterprise Information Systems - 11th International Conference, ICEIS 2009, Proceedings",
address = "Germany",
note = "11th International Conference on Enterprise Information Systems, ICEIS 2009 ; Conference date: 06-05-2009 Through 10-05-2009",
}