Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
The VLDB Journal — The International Journal on Very Large Data Bases
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Towards a robust fuzzy clustering
Fuzzy Sets and Systems - Data analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust maximum entropy clustering algorithm with its labeling for outliers
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A convergence theorem for the fuzzy subspace clustering (FSC) algorithm
Pattern Recognition
Feature extraction through local learning
Statistical Analysis and Data Mining
IEEE Transactions on Fuzzy Systems
Minimum-maximum local structure information for feature selection
Pattern Recognition Letters
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
| Hi-index | 0.00 |
A latest advance in Relief-feature-weighting techniques is that the iterative procedure of Relief can be approximately expressed as a margin maximization problem, and therefore, its distinctive properties can be investigated with the help of optimization theory. Being motivated by this advance, the Relief-featureweighting algorithm is investigated for the first time within a fuzzyoptimization framework. A new margin-based objective function that incorporates three fuzzy concepts, namely, fuzzy-difference measure, fuzzy-feature weighting, and fuzzy-instance force coefficient, is introduced. By the application of fuzzy optimization to this new margin-based objective function, several useful theoretical results are derived, based upon which, a set of robust Relief-featureweighting algorithms are proposed for two-class data, multi class data, and, then, online data. As demonstrated by extensive experiments in synthetic datasets, the University of California at Irvine (UCI)-benchmark datasets, cancer-gene-expression datasets, and face-image datasets, the proposed algorithms were found to be competitive with the state-of-the-art algorithms and robust for datasets with noise and/or outliers.