Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Decomposition of Task Specification Problems
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Data reduction: discretization of numerical attributes
Handbook of data mining and knowledge discovery
An introduction to variable and feature selection
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Decomposition methodology for classification tasks: a meta decomposer framework
Pattern Analysis & Applications
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Missing template decomposition method and its implementation in rough set exploration system
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Finding rough and fuzzy-rough set reducts with SAT
Information Sciences: an International Journal
Hi-index | 12.05 |
Feature selection is a key issue in the research on rough set theory. However, when handling large-scale data, many current feature selection methods based on rough set theory are incapable. In this paper, two novel feature selection methods are put forward based on decomposition and composition principles. The idea of decomposition and composition is to break a complex table down into a master-table and several sub-tables that are simpler, more manageable and more solvable by using existing induction methods, then joining them together in order to solve the original table. Compared with some traditional methods, the efficiency of the proposed algorithms can be illustrated by experiments with standard datasets from UCI database.