On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Issues in data stream management
ACM SIGMOD Record
ACM SIGMOD Record
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Pattern Trees Induction: A New Machine Learning Method
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Top-Down Induction of Fuzzy Pattern Trees
IEEE Transactions on Fuzzy Systems
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
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Fuzzy pattern trees (FPTs) have recently been introduced as a novel model class for machine learning. In this paper, we consider the problem of learning fuzzy pattern trees for binary classification from data streams. Apart from its practical relevance, this problem is also interesting from a methodological point of view. First, the aspect of efficiency plays an important role in the context of data streams, since learning has to be accomplished under hard time (and memory) constraints. Moreover, a learning algorithm should be adaptive in the sense that an up-to-date model is offered at any time, taking new data items into consideration as soon as they arrive and perhaps forgetting old ones that have become obsolete due to a change of the underlying data generating process. To meet these requirements, we develop an evolving version of fuzzy pattern tree learning, in which model adaptation is realized by anticipating possible local changes of the current model, and confirming these changes through statistical hypothesis testing. In experimental studies, we compare our method to a state-of-the-art tree-based classifier for learning from data streams, showing that evolving pattern trees are competitive in terms of performance while typically producing smaller and more compact models.