Learning from data stream based on Random Projection and Hoeffding Tree classifier
Author(s)
Xuan, Cuong Pham
Manh, Truong Dang
Sang, Viet Dinh
Son, Hoang
Tien, Thanh Nguyen
Liew, Alan Wee-Chung
Griffith University Author(s)
Year published
2017
Metadata
Show full item recordAbstract
In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding tree classifiers. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed approach performs significantly better than the single Hoeffding tree and some well-known online ...
View more >In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding tree classifiers. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed approach performs significantly better than the single Hoeffding tree and some well-known online learning algorithms including additive models and Online Bagging.
View less >
View more >In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding tree classifiers. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed approach performs significantly better than the single Hoeffding tree and some well-known online learning algorithms including additive models and Online Bagging.
View less >
Conference Title
2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
Volume
2017-December
Subject
Artificial intelligence