Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Computers and Industrial Engineering
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Consistency-based search in feature selection
Artificial Intelligence
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of rough set methods and representative inductive learning algorithms
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Combined SVM-Based Feature Selection and Classification
Machine Learning
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature subset selection wrapper based on mutual information and rough sets
Expert Systems with Applications: An International Journal
Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system
Information Sciences: an International Journal
Efficient ant colony optimization for image feature selection
Signal Processing
Investigating memetic algorithm in solving rough set attribute reduction
International Journal of Computer Applications in Technology
Finding rough and fuzzy-rough set reducts with SAT
Information Sciences: an International Journal
Immune ant swarm optimization for optimum rough reducts generation
International Journal of Hybrid Intelligent Systems
Hi-index | 0.10 |
Rough set theory is one of the effective methods to feature selection, which can preserve the meaning of the features. The essence of rough set approach to feature selection is to find a subset of the original features. Since finding a minimal subset of the features is a NP-hard problem, it is necessary to investigate effective and efficient heuristic algorithms. Ant colony optimization (ACO) has been successfully applied to many difficult combinatorial problems like quadratic assignment, traveling salesman, scheduling, etc. It is particularly attractive for feature selection since there is no heuristic information that can guide search to the optimal minimal subset every time. However, ants can discover the best feature combinations as they traverse the graph. In this paper, we propose a new rough set approach to feature selection based on ACO, which adopts mutual information based feature significance as heuristic information. A novel feature selection algorithm is also given. Jensen and Shen proposed a ACO-based feature selection approach which starts from a random feature. Our approach starts from the feature core, which changes the complete graph to a smaller one. To verify the efficiency of our algorithm, experiments are carried out on some standard UCI datasets. The results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features.