Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Discrete Mathematical Structures
Discrete Mathematical Structures
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Dynamic Discretization of Continuous Attributes
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Approximations and reducts with covering generalized rough sets
Computers & Mathematics with Applications
International Journal of Approximate Reasoning
A Rough Set Based Hybrid Method to Feature Selection
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
Attribute dependency functions considering data efficiency
International Journal of Approximate Reasoning
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
Reduction about approximation spaces of covering generalized rough sets
International Journal of Approximate Reasoning
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
Aggregating multiple classification results using fuzzy integration and stochastic feature selection
International Journal of Approximate Reasoning
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
An efficient rough feature selection algorithm with a multi-granulation view
International Journal of Approximate Reasoning
FRPS: A Fuzzy Rough Prototype Selection method
Pattern Recognition
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.00 |
Rough set feature selection (RSFS) can be used to improve classifier performance. RSFS removes redundant attributes whilst retaining important ones that preserve the classification power of the original dataset. Reducts are feature subsets selected by RSFS. Core is the intersection of all the reducts of a dataset. RSFS can only handle discrete attributes, hence, continuous attributes need to be discretized before being input to RSFS. Discretization determines the core size of a discrete dataset. However, current discretization methods do not consider the core size during discretization. Earlier work has proposed core-generating approximate minimum entropy discretization (C-GAME) algorithm which selects the maximum number of minimum entropy cuts capable of generating a non-empty core within a discrete dataset. The contributions of this paper are as follows: (1) the C-GAME algorithm is improved by adding a new type of constraint to eliminate the possibility that only a single reduct is present in a C-GAME-discrete dataset; (2) performance evaluation of C-GAME in comparison to C4.5, multi-layer perceptrons, RBF networks and k-nearest neighbours classifiers on ten datasets chosen from the UCI Machine Learning Repository; (3) performance evaluation of C-GAME in comparison to Recursive Minimum Entropy Partition (RMEP), Chimerge, Boolean Reasoning and Equal Frequency discretization algorithms on the ten datasets; (4) evaluation of the effects of C-GAME and the other four discretization methods on the sizes of reducts; (5) an upper bound is defined on the total number of reducts within a dataset; (6) the effects of different discretization algorithms on the total number of reducts are analysed; (7) performance analysis of two RSFS algorithms (a genetic algorithm and Johnson's algorithm).