The online performance estimation framework: heterogeneous ensemble learning for data streams
نویسندگان
چکیده
منابع مشابه
Online Ensemble Learning for Imbalanced Data Streams
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within th...
متن کاملHeterogeneous Ensemble for Feature Drifts in Data Streams
The nature of data streams requires classification algorithms to be real-time, efficient, and able to cope with high-dimensional data that are continuously arriving. It is a known fact that in high-dimensional datasets, not all features are critical for training a classifier. To improve the performance of data stream classification, we propose an algorithm called HEFT-Stream (Heterogeneous Ense...
متن کاملRobust ensemble learning for mining noisy data streams
a Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China b Centre for Quantum Computation & Intelligent Systems, University of Technology Sydney, Broadway, NSW 2007, Australia c Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China d College of Information Science & Technology, Univ. of Nebraska at Omaha, Omaha, NE 68...
متن کاملOnline Machine Learning in Big Data Streams
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is...
متن کاملLearning from data streams via online transduction
A practical issue in the existing transduction methods is expensive and inefficient computation compared to induction methods. This has hindered the use of transduction methods in temporal and real-time data mining. In this paper, we introduce a fast incremental transductive confidence machine (TCM) based on adiabatic incremental support vector machine (SVM) such that critical information from ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2017
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-017-5686-9