Learning in the presence of concept drift and hidden contexts
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Machine Learning
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Data Mining for Very Busy People
Computer
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On the complexity of finding emerging patterns
Theoretical Computer Science - Pattern discovery in the post genome
IEEE Transactions on Knowledge and Data Engineering
Using multiple windows to track concept drift
Intelligent Data Analysis
A Top-Down and Greedy Method for Discretization of Continuous Attributes
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
A Decision Tree-Based Approach to Mining the Rules of Concept Drift
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
An evolutionary and attribute-oriented ensemble classifier
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Structure learning for belief rule base expert system: A comparative study
Knowledge-Based Systems
An adaptive ensemble classifier for mining concept drifting data streams
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In a database, the concept of an example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using the old dataset is not suitable for predicting a new dataset. Therefore, the problem of concept drift has attracted a lot of attention in recent years. Although many algorithms have been proposed to solve this problem, they have not been able to provide users with a satisfactory solution to concept drift. That is, the current research about concept drift focuses only on updating the classification model. However, real life decision makers might be very interested in the rules of concept drift. For example, doctors desire to know the root causes behind variation in the causes and development of disease. In this paper, we propose a concept drift rule mining tree, called CDR-Tree, to accurately discover the underlying rule governing concept drift. The main contributions of this paper are: (a) we address the problem of mining concept-drifting rules which has not been considered in previously developed classification schemes; (b) we develop a method that can accurately mine rules governing concept drift; (c) we develop a method that should classification models be required, can efficiently and accurately generate such models via a simple extraction procedure rather than constructing them anew; and (d) we propose two strategies to reduce the complexity of concept-drifting rules mined by our CDR-Tree.