Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty

Authors:
José R. Villar;María R. Suárez;Javier Sedano;Felipe Mateos
Affiliations:
Computer Science Department, University of Oviedo, Gijón, Spain 33204;Computer Science Department, University of Oviedo, Gijón, Spain 33204;Electromechanic Engineering Department, University of Burgos, Spain;Electric, Electronic, Computers and Systems Engineering Department, University of Oviedo, Gijón, Spain 33204
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

Citing 12
Cited 1

The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems

Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Learning fuzzy classification rules from labeled data

Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Feature selection based on a modified fuzzy C-means algorithm with supervision

Information Sciences—Informatics and Computer Science: An International Journal
Consensus unsupervised feature ranking from multiple views

Pattern Recognition Letters
Unsupervised Feature Selection for Detection Using Mutual Information Thresholding

WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Review: Dimensionality reduction based on rough set theory: A review

Applied Soft Computing
KEEL: a software tool to assess evolutionary algorithms for data mining problems

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
A New Feature Selection Scheme Using a Data Distribution Factor for Unsupervised Nominal Data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy probabilistic approximation spaces and their information measures

IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection

IEEE Transactions on Fuzzy Systems

Soft computing decision support for a steel sheet incremental cold shaping process

IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and uncertainty have still not been studied. In this work, an extension of the Fuzzy Mutual Information Feature Selection algorithm for unsupervised problems is outlined. This proposal is a two stage procedure. Firstly, it makes use of the fuzzy mutual information measure and Battiti's feature selection algorithm and of a genetic algorithm to analyze the relationships between feature subspaces in a high dimensional space. The second stage uses a simple ad hoc heuristic with the aim to extract the most relevant relationships. It is concluded, given the results from the experiments carried out in this preliminary work, that it is possible to apply frequent pattern mining or similar methods in the second stage to reduce the dimensionality of the data set.