On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Machine Learning
A review of feature selection techniques in bioinformatics
Bioinformatics
International Journal of Approximate Reasoning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A classification and regression technique to handle heterogeneous and imperfect information
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Approximate Reasoning
Variable selection using random forests
Pattern Recognition Letters
Feature selection using fuzzy entropy measures with similarity classifier
Expert Systems with Applications: An International Journal
Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems
Fuzzy Sets and Systems
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
Fuzzy criteria for feature selection
Fuzzy Sets and Systems
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Extending information processing in a Fuzzy Random Forest ensemble
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Knowledge Extraction from Low Quality Data: Theoretical, Methodological and Practical Issues
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
An unsupervised approach to feature discretization and selection
Pattern Recognition
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Today, feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, as well as redundant features that are strongly correlated. There are a lot of approaches for feature selection, but most of them can only work with crisp data. Until now there have not been many different approaches which can directly work with both crisp and low quality (imprecise and uncertain) data. That is why, we propose a new method of feature selection which can handle both crisp and low quality data. The proposed approach is based on a Fuzzy Random Forest and it integrates filter and wrapper methods into a sequential search procedure with improved classification accuracy of the features selected. This approach consists of the following main steps: (1) scaling and discretization process of the feature set; and feature pre-selection using the discretization process (filter); (2) ranking process of the feature pre-selection using the Fuzzy Decision Trees of a Fuzzy Random Forest ensemble; and (3) wrapper feature selection using a Fuzzy Random Forest ensemble based on cross-validation. The efficiency and effectiveness of this approach is proved through several experiments using both high dimensional and low quality datasets. The approach shows a good performance (not only classification accuracy, but also with respect to the number of features selected) and good behavior both with high dimensional datasets (microarray datasets) and with low quality datasets.