Instance-Based Learning Algorithms
Machine Learning
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Adaptive global optimization with local search
Adaptive global optimization with local search
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Nearest Neighbor Search: A Database Perspective
Nearest Neighbor Search: A Database Perspective
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Design of nearest neighbor classifiers: multi-objective approach
International Journal of Approximate Reasoning
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
A Vehicle Routing Problem Solved by Agents
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A review of instance selection methods
Artificial Intelligence Review
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
Prototype reduction techniques: A comparison among different approaches
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Enhancing IPADE algorithm with a different individual codification
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Editorial: Large scale instance selection by means of federal instance selection
Data & Knowledge Engineering
InstanceRank based on borders for instance selection
Pattern Recognition
A scalable approach to simultaneous evolutionary instance and feature selection
Information Sciences: an International Journal
FRPS: A Fuzzy Rough Prototype Selection method
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
Linear reconstruction measure steered nearest neighbor classification framework
Pattern Recognition
IDS false alarm reduction using an instance selection KNN-memetic algorithm
International Journal of Metaheuristics
Information Sciences: an International Journal
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
Hi-index | 0.01 |
Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem. Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature. The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up.