Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
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Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
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On the practical applicability of VC dimension bounds
Neural Computation
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
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The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
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Efficient GA Based Techniques for Classification
Applied Intelligence
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Combining Multiple K-Nearest Neighbor Classifiers for Text Classification by Reducts
DS '02 Proceedings of the 5th International Conference on Discovery Science
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Ensemble Feature election with the Simple Bayesian Classification in Medical Diagnostics
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
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IEEE Transactions on Knowledge and Data Engineering
Learning Ensembles from Bites: A Scalable and Accurate Approach
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Information Sciences: an International Journal - Special issue: Soft computing data mining
Multiknowledge for decision making
Knowledge and Information Systems
Decomposition methodology for classification tasks: a meta decomposer framework
Pattern Analysis & Applications
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature set decomposition for decision trees
Intelligent Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Constructing rough decision forests
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Feature selection in bankruptcy prediction
Knowledge-Based Systems
Computational Statistics & Data Analysis
Artificial Intelligence Review
Creating ensembles of classifiers via fuzzy clustering and deflection
Fuzzy Sets and Systems
An improved genetic algorithm for optimal feature subset selection from multi-character feature set
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Information Sciences: an International Journal
Selective voting in convex-hull ensembles improves classification accuracy
Artificial Intelligence in Medicine
How to reduce dimension while improving performance
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
An efficient ensemble classification method based on novel classifier selection technique
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
Data Mining and Knowledge Discovery
Dynamic facial expression analysis based on extended spatio-temporal histogram of oriented gradients
International Journal of Biometrics
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Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a single useful subset of features. This paper presents a novel feature set partitioning approach that is based on a genetic algorithm. As part of this new approach a new encoding schema is also proposed and its properties are discussed. We examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of multiple, oblivious tree classifiers. The new algorithm was tested on various datasets and the results indicate the superiority of the proposed algorithm to other methods.