@inproceedings{e8b950e9f22644c58ef9fa7c75f50e67,
title = "Real-time stream mining electric power consumption data using hoeffding tree with shadow features",
abstract = "Many energy load forecasting models have been established from batch-based supervised learning models where the whole data must be loaded to learn. Due to the sheer volumes of the accumulated consumption data which arrive in the form of continuous data streams, such batch-mode learning requires a very long time to rebuild the model. Incremental learning, on the other hand, is an alternative for online learning and prediction which learns the data stream in segments. However, it is known that its prediction performance falls short when compared to batch learning. In this paper, we propose a novel approach called Shadow Features (SF) which offer extra dimensions of information about the data streams. SF are relatively easy to compute, suitable for lightweight online stream mining.",
keywords = "Data stream mining, Electric power consumption prediction, Shadow features",
author = "Simon Fong and Meng Yuen and Wong, {Raymond K.} and Wei Song and Kyungeun Cho",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 12th International Conference on Advanced Data Mining and Applications, ADMA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",
year = "2016",
doi = "10.1007/978-3-319-49586-6_56",
language = "English",
isbn = "9783319495859",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "775--786",
editor = "Shuliang Wang and Jinyan Li and Jianxin Li and Xue Li and Sheng, {Quan Z.}",
booktitle = "Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Proceedings",
address = "Germany",
}