Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
A perspective view and survey of meta-learning
Artificial Intelligence Review
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
New synthesis of bayesian network classifiers and cardiac spect image interpretation
New synthesis of bayesian network classifiers and cardiac spect image interpretation
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Bayesian network classifiers: Beyond classification accuracy
Intelligent Data Analysis
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Bayesian learning for cardiac SPECT image interpretation
Artificial Intelligence in Medicine
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolving evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
When faced with a new machine learning problem, selecting which classifier is the best to perform the task at hand is a very hard problem. Most solutions proposed in the literature are based on meta-learning, and use meta-data about the problem to recommend an effective algorithm to solve the task. This paper proposes a new approach to this problem: to build an algorithm tailored to the application problem at hand. More specifically, we propose an evolutionary algorithm (EA) to automatically evolve Bayesian Network Classifiers (BNCs). The method receives as input a list of the main components of BNC algorithms, and uses an EA to encode these components. Given an input dataset, the method tests different combinations of components to that specific application domain. The method was tested in 10 UCI datasets, and compared to three classical BNCs and a greedy search algorithm. Results show that the current algorithms can indeed be improved, but that the EA is currently outperformed by the greedy search.