Fuzzy Information Measures Research Articles

Mutual Information Analysis with Similarity Measure

Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

A feature selection method using a fuzzy mutual information measure

A feature selection method using a fuzzy mutual information measure

Supervised feature subset selection using extended fuzzy absolute information measure for handling different discretized datasets

Abstract Feature subset selection is applied in wide range of domains like biometrics, chemistry, text processing, pattern recognition, speech processing and vision. Most of the prior work in biometrics and other applications have emphasized the importance of feature extraction and classification. However, the critical issue of examining the usefulness of extracted features has been largely ignored. But feature subset selection helps to identify and remove much of the irrelevant and redundant features. It reduces the dimensions of datasets. So it avoids the problems faced in feature extraction. The proposed Extended Fuzzy Absolute Information Measure (EFAIM) is applied to select feature subsets by focusing on boundary samples. The proposed method can select feature subsets with the minimum number of features. The experimental results with UCI datasets show that the proposed method is effective and efficient in selecting subset with minimum number of features than fuzzy entropy measure. This experiment shows that among the given number of features in the datasets, a small relevant subset of features is only selected for feature subset. Thus the selected subset of features is necessary in practice for building an accurate result.

Novel fuzzy information proximity measures

As a measure of information shared between two fuzzy pattern vectors, the fuzzy information proximity measure (FIPM) plays an important part in fuzzy pattern recognition, fuzzy clustering analysis and fuzzy approximate reasoning. In this paper, two novel FIPMs are set up. Firstly, an axiom theory about the FIPM is given, and different expressions of the FIPM are discussed. A new FIPM is then proposed based on the axiom theory of the FIPM and the concept of fuzzy subsethood function. Two concepts based on the idea of Shannon information entropy, fuzzy joint entropy (FJE) and fuzzy conditional entropy (FCE), are proposed and the basic properties of FJE and FCE are given and proved. Finally, classical similarity measures such as dissimilarity measure (DM) and similarity measure (SM) are studied, and two new measures, fuzzy absolute information measure (FAIM) and fuzzy relative information measure (FRIM), are set up, which can be used as measures of the proximity between fuzzy sets A and B.

A note on fuzzy information measures

A new entropy of a fuzzy set is defined based on the membership functions of the intersection and union of the set and its complement set. The concept of fuzzy cross-entropy is also introduced and its definition is given by analogy with the cross-entropy of probability distributions. Their possible applications are highlighted.

Motion Frame Analysis and Scene Abstraction: Discrimination Ability of Fuzziness Measures

This article addresses a solution to the problem of scene estimation/abstraction of motion video data in the fuzzy set theoretic framework. Using various fuzzy geometrical and information measures as image features, an algorithm is developed to compute the change of information in each of two successive frames to classify scenes/frames. Frame similarity is measured in terms of weighted distance in fuzzy feature space. This categorization process of raw input visual data can be used to establish structure for correlation. The investigation not only attempts to determine the discrimination ability of the fuzziness measures for classifying scenes, but also enhances the capability of nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Color and language as man-machine interface parameters

Growing use of terminals for CAD/CAM as well as the general proliferation of work stations justifies focusing attention on effective communication at the terminal interface. Color is often used to enhance the flow of information even though many aspects of color are still not well understood. Close scrutiny of the color phenomena using fuzzy set concepts as well as conventional techniques has yielded quantitative results consistent with available folk theorems. These results and some new insight on earlier color selection methods are presented concisely. We build on our previous results: a 20 gray level criterion and the quantitative transformation yielding the number of colors C corresponding to a given number of gray levels L. This relation C = 0.074L3 and a fuzzy entropy-based information measure are found to fit closely a lognormal distribution. Natural (not computer) languages present a different problem because of more ambiguity and variability. the bridge here stems from the observation that corresponding to the upper bound of about 200 gray levels for vision, we find that the human larynx can produce about 200 distinctive sounds which is the key to language capability. We focus on English because it is an important special case for international technical and scientific discourse. Our concepts and techniques are extended to yield some initial informal results concerning usable vaocabulary. Based on the latter, we present a general display for number of gay levels, color, and English words.