@inproceedings{19466d4698094e2f92b4ee4fd5a2652d,
title = "Dengesiz veri akimlari i{\c c}in dengelenmi{\c s} rassal orman",
abstract = "Data with highly imbalanced class distributions are common in real life. Machine learning application domains such as e-commerce, risk management, environmental, and health monitoring often suffer from class imbalance since the interesting case occurs rarely. Yet another layer of complexity is added when data arrives as massive streams. In such a setting, it is often of interest that a learning algorithm is updated in an incremental fashion for scalability and model adaptivity reasons while still handling the class imbalance. In this paper, we propose an ensemble algorithm for imbalanced data streams based on the offline balanced random forest idea. We also show on a recent dataset that the algorithm is useful for the buyer prediction problem in large-scale recommender systems.",
keywords = "Classifier ensembles, Data stream learning, Recommender systems",
author = "Yaǧci, {A. Murat} and Tevfik Aytekin and G{\"u}rgen, {Fikret S.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",
year = "2016",
month = jun,
day = "20",
doi = "10.1109/SIU.2016.7495927",
language = "T{\"u}rk{\c c}e",
series = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1065--1068",
booktitle = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",
}