Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
A random sets-based method for identifying fuzzy models
Fuzzy Sets and Systems
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Self-generating prototypes for pattern classification
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
International Journal of Intelligent Systems
Data set Editing by Ordered Projection
Intelligent Data Analysis
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
Top 10 algorithms in data mining
Knowledge and Information Systems
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
Class Conditional Nearest Neighbor for Large Margin Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of instance selection methods
Artificial Intelligence Review
Information Sciences: an International Journal
Information Sciences: an International Journal
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear separability and classification complexity
Expert Systems with Applications: An International Journal
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
An algorithm for a selective nearest neighbor decision rule (Corresp.)
IEEE Transactions on Information Theory
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sciences: an International Journal
A meta-learning prediction model of algorithm performance for continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Load forecasting using a multivariate meta-learning system
Expert Systems with Applications: An International Journal
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Many authors agree that, when applying instance selection to a data set, it would be useful to characterize the data set in order to choose the most suitable selection criterion. Based on this hypothesis, we propose an architecture for knowledge-based instance selection (KBIS) systems. It uses meta-learning to select the best suited instance selection method for each specific database, among several methods available. We carried out a study in order to verify whether this architecture can outperform the individual methods. Two different versions of a KBIS system based on our architecture, each using a different learner, were instantiated. They were evaluated experimentally and the results were compared to those of the individual methods used.