Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to the Special Issue on Meta-Learning
Machine Learning
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Incremental Learning of Linear Model Trees
Machine Learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Online classification of nonstationary data streams
Intelligent Data Analysis
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Combining Online Classification Approaches for Changing Environments
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Self-Adaptive Induction of Regression Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robustness of change detection algorithms
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
A few useful things to know about machine learning
Communications of the ACM
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
Meta-Learning for Periodic Algorithm Selection in Time-Changing Data
SBRN '12 Proceedings of the 2012 Brazilian Symposium on Neural Networks
| Hi-index | 0.01 |
Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an appropriate model at a time point may rapidly become obsolete, requiring updating or replacement. As there are several learning algorithms available, choosing one whose bias suits the current data best is not a trivial task. In this paper, we present a meta-learning based method for periodic algorithm selection in time-changing environments, named MetaStream. It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination. Experimental results for two real regression problems showed that MetaStream is able to improve the general performance of the learning system compared to a baseline method and an ensemble-based approach.