Instance-Based Learning Algorithms
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Case-Based Reasoning Technology, From Foundations to Applications
Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental learning with partial instance memory
Artificial Intelligence
Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Online classification of nonstationary data streams
Intelligent Data Analysis
Dynamic integration of classifiers for handling concept drift
Information Fusion
Mining competent case bases for case-based reasoning
Artificial Intelligence
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
An incremental cluster-based approach to spam filtering
Expert Systems with Applications: An International Journal
Optimal Window Change Detection
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Outlier detection using default reasoning
Artificial Intelligence
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Informed case base maintenance: a complexity profiling approach
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Adaptive concept drift detection
Statistical Analysis and Data Mining - Best of SDM'09
A hybrid approach to outlier detection in the offset lithographic printing process
Engineering Applications of Artificial Intelligence
Mining Concept-Drifting and Noisy Data Streams Using Ensemble Classifiers
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 04
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
Quick adaptation to changing concepts by sensitive detection
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Complexity profiling for informed case-base editing
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Incremental Learning of Concept Drift in Nonstationary Environments
IEEE Transactions on Neural Networks
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Detecting changes of concepts, such as a change of customer preference for telecom services, is very important in terms of prediction and decision applications in dynamic environments. In particular, for case-based reasoning systems, it is important to know when and how concept drift can effectively assist decision makers to perform smarter maintenance operations at an appropriate time. This paper presents a novel method for detecting concept drift in a case-based reasoning system. Rather than measuring the actual case distribution, we introduce a new competence model that detects differences through changes in competence. Our competence-based concept detection method requires no prior knowledge of case distribution and provides statistical guarantees on the reliability of the changes detected, as well as meaningful descriptions and quantification of these changes. This research concludes that changes in data distribution do reflect upon competence. Eight sets of experiments under three categories demonstrate that our method effectively detects concept drift and highlights drifting competence areas accurately. These results directly contribute to the research that tackles concept drift in case-based reasoning, and to competence model studies.