Advances in instance selection for instance-based learning algorithms
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Publication:1597453
DOI10.1023/A:1014043630878zbMath1027.68673MaRDI QIDQ1597453
Publication date: 30 May 2002
Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)
Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies and applications (68U99)
Related Items (23)
Linear reconstruction measure steered nearest neighbor classification framework ⋮ GEOMETRIC PROXIMITY GRAPHS FOR IMPROVING NEAREST NEIGHBOR METHODS IN INSTANCE-BASED LEARNING AND DATA MINING ⋮ A lightweight data preprocessing strategy with fast contradiction analysis for incremental classifier learning ⋮ Evolutionary feature selection for big data classification: a MapReduce approach ⋮ IRAHC: instance reduction algorithm using hyperrectangle clustering ⋮ FRPS: a fuzzy rough prototype selection method ⋮ NP-hardness of some data cleaning problem ⋮ A class boundary preserving algorithm for data condensation ⋮ A concept drift-tolerant case-base editing technique ⋮ Boosting \(k\)-NN for categorization of natural scenes ⋮ A cooperative coevolutionary algorithm for instance selection for instance-based learning ⋮ Bayesian instance selection for the nearest neighbor rule ⋮ METHOD OF KEY VECTORS EXTRACTION USING R-CLOUD CLASSIFIERS ⋮ A memetic algorithm for evolutionary prototype selection: A scaling up approach ⋮ From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review ⋮ Concept drift detection via competence models ⋮ An instance level analysis of data complexity ⋮ IFS-CoCo: instance and feature selection based on cooperative coevolution with nearest neighbor rule ⋮ Identifying predictive hubs to condense the training set of \(k\)-nearest neighbour classifiers ⋮ Rough-fuzzy weighted \(k\)-nearest leader classifier for large data sets ⋮ Incremental exemplar learning schemes for classification on embedded devices ⋮ Comparison of prototype selection algorithms used in construction of neural networks learned by SVD ⋮ Michigan particle swarm optimization for prototype reduction in classification problems
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