Discriminative Power of Input Features in a Fuzzy Model

Authors:
Rosaria Silipo;Michael R. Berthold
Affiliations:
-;-
Venue:
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Year:
1999

Citing 2
Cited 3

Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods
Fuzzy decision trees: issues and methods

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Supervised neuro-fuzzy clustering for life science applications

ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions

Artificial Intelligence in Medicine
Finding optimal decision scores by evolutionary strategies

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, associated with the use of a given input dimension, characterizes the input feature's discriminative power. Due to the low computational expenses derived from the use of a fuzzy model, the proposed possibilistic information gain generates a simple and efficient algorithm for the reduction of the input dimensionality, even for high dimensional cases. As real-world example, the most informative electrocardiographic measures are detected for an arrhythmia classification problem.