Extending information processing in a Fuzzy Random Forest ensemble

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty, and imprecision that can be handled. In this paper...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2012-05, Vol.16 (5), p.845-861
Main Authors: Cadenas, Jose M., Garrido, M. Carmen, Martínez, Raquel, Bonissone, Piero P.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Control
Data processing
Datasets
Decision trees
Engineering
Focus
Fuzzy sets
Mathematical Logic and Foundations
Mechatronics
Probability
Probability distribution
Robotics
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty, and imprecision that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble for classification and show its ability to handle imperfect data into the learning and the classification phases. Then, we will describe the types of imperfect data it supports. We will devise an augmented ensemble that can operate with others type of imperfect data: crisp, missing, probabilistic uncertainty, and imprecise (fuzzy and crisp) values. Additionally, we will perform experiments with imperfect datasets created for this purpose and datasets used in other papers to show the advantage of being able to express the true nature of imperfect information.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-011-0777-1