Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Communications of the ACM - Special issue on parallelism
Proceedings of the third international conference on Genetic algorithms
Foundations of cognitive science
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms
Machine Learning
Similarity and analogical reasoning
Similarity and analogical reasoning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
PLEASE: A Prototype Learning System Using Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
Focusing solutions for data mining: analytical studies and experimental results in real-world domains
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the proposed system.