In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently ...
Bibliographic details on A scalable evolutionary learning classifier system for knowledge discovery in stream data mining.
ExSTraCS 2.0: Description and Evaluation of a Scalable Learning ...
pmc.ncbi.nlm.nih.gov › PMC4583133
This allows the algorithm to flexibly and effectively describe complex and diverse problem spaces found in behavior modeling, function approximation, ...
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (eg typically a genetic algorithm)
ExSTraCS is a RBML algorithm developed by our research group which combines evolutionary learning with a 'piece-wise' knowledge representation comprised of a ...
This paper proposes a new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size ...
Jun 20, 2024 · This paper proposes a new online single pass framework for stream data mining, namely Scalable Concept Drift Adaptation (SCDA), and presents three distinct ...
This paper explores parallel and distributed implementation of the Learning Classifier System (LCS) technology. Specifically, the adaptation of supervised ...
In this paper, we propose a Fast Evolutionary Algorithm for Clustering data streams (FEAC-Stream) that allows estimating k automatically from data in an online ...
Missing: classifier | Show results with:classifier
In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of ...
Missing: evolutionary | Show results with:evolutionary