Evaluation of adaptive mixtures of competing experts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
IEEE Transactions on Pattern Analysis and Machine Intelligence
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
Machine Learning
A new hybrid approach in combining multiple experts to recognise handwritten numerals
Pattern Recognition Letters
Journal of the ACM (JACM)
Combining predictors: comparison of five meta machine learning methods
Information Sciences: an International Journal
FANNC: a fast adaptive neural network classifier
Knowledge and Information Systems
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Growing and Pruning Neural Tree Networks
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Transformation by Function Decomposition
IEEE Intelligent Systems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning stable concepts in a changing world
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications (Machine Perception and Artificial Intelligence)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Intelligent Data Analysis
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Data gravitation based classification
Information Sciences: an International Journal
Computational Statistics & Data Analysis
Multivariable stream data classification using motifs and their temporal relations
Information Sciences: an International Journal
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
Artificial Intelligence Review
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
Expert Systems with Applications: An International Journal
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Data envelopment analysis classification machine
Information Sciences: an International Journal
A meta learning approach to grammatical error correction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
| Hi-index | 0.07 |
This paper examines a decision-tree framework for instance-space decomposition. According to the framework, the original instance-space is hierarchically partitioned into multiple subspaces and a distinct classifier is assigned to each subspace. Subsequently, an unlabeled, previously-unseen instance is classified by employing the classifier that was assigned to the subspace to which the instance belongs. After describing the framework, the paper suggests a novel splitting-rule for the framework and presents an experimental study, which was conducted, to compare various implementations of the framework. The study indicates that using the novel splitting-rule, previously presented implementations of the framework, can be improved in terms of accuracy and computation time.