Artificial Intelligence Review - Special issue on lazy learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Feature Weighting in k-Means Clustering
Machine Learning
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
MACLAW: A modular approach for clustering with local attribute weighting
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
Exploitation of a parallel clustering algorithm on commodity hardware with P2P-MPI
The Journal of Supercomputing
Genetic algorithms for feature weighting: evolution vs. coevolution and darwin vs. lamarck
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
An organizational coevolutionary algorithm for classification
IEEE Transactions on Evolutionary Computation
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Unsupervised feature weighting with multi niche crowding genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Clustered-Hybrid Multilayer Perceptron network for pattern recognition application
Applied Soft Computing
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Feature weighting is an aspect of increasing importance in clustering because data are becoming more and more complex nowadays. In this paper, we propose two new feature weighting methods based on coevolutive algorithms. The first one is inspired by the Lamarck theory (inheritance of acquired characteristics) and uses the distance-based cost function defined in the LKM algorithm as fitness function. The second method uses a fitness function based on a new partitioning quality measure. It does not need a distance-based measure. We compare classical hill-climbing optimization with these new genetic algorithms on three data sets from UCI. Results show that the proposed methods are better than the hill-climbing based algorithms. We also present a process of hyperspectral remotely sensed image classification. The experiments, corroborated by geographers, highlight the benefits of using coevolutionary feature weighting methods to improve knowledge discovery process.