TY - GEN
T1 - Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift?
AU - Lu, Yang
AU - CHEUNG, Yiu Ming
AU - Tang, Yuan Yan
N1 - Funding Information:
∗Yiu-ming Cheung is the corresponding author. This work was supported by the National Natural Science Foundation of China with the Grant Numbers: 61672444 and 61272366, by the SZSTI Grant: JCYJ20160531194006833, and by the Faculty Research Grant of Hong Kong Baptist University (HKBU) with the Project Codes: FRG2/16-17/051 and FRG2/15-16/049.
PY - 2017/8
Y1 - 2017/8
N2 - Concept drifts occurring in data streams will jeopardize the accuracy and stability of the online learning process. If the data stream is imbalanced, it will be even more challenging to detect and cure the concept drift. In the literature, these two problems have been intensively addressed separately, but have yet to be well studied when they occur together. In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem. DWMIL utilizes an ensemble framework by dynamically weighting the base classifiers according to their performance on the current data chunk. Compared with the existing methods, its merits are four-fold: (1) it can keep stable for non-drifted streams and quickly adapt to the new concept; (2) it is totally incremental, i.e. no previous data needs to be stored; (3) it keeps a limited number of classifiers to ensure high efficiency; and (4) it is simple and needs only one thresholding parameter. Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.
AB - Concept drifts occurring in data streams will jeopardize the accuracy and stability of the online learning process. If the data stream is imbalanced, it will be even more challenging to detect and cure the concept drift. In the literature, these two problems have been intensively addressed separately, but have yet to be well studied when they occur together. In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem. DWMIL utilizes an ensemble framework by dynamically weighting the base classifiers according to their performance on the current data chunk. Compared with the existing methods, its merits are four-fold: (1) it can keep stable for non-drifted streams and quickly adapt to the new concept; (2) it is totally incremental, i.e. no previous data needs to be stored; (3) it keeps a limited number of classifiers to ensure high efficiency; and (4) it is simple and needs only one thresholding parameter. Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.
UR - https://www.ijcai.org/proceedings/2017/
UR - http://www.scopus.com/inward/record.url?scp=85031901669&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2017/333
DO - 10.24963/ijcai.2017/333
M3 - Conference contribution
AN - SCOPUS:85031901669
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2393
EP - 2399
BT - Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Y2 - 19 August 2017 through 25 August 2017
ER -