Definition and construction of fuzzy DI-subsethood measures

Bustince, Humberto; Mohedano, V.; Barrenechea, Edurne; Pagola, Miguel

doi:10.1016/j.ins.2005.06.006

Cited by 113 publications

(57 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many more subsethood measures have been developed and reported in literature [37]. However, in the rest of the paper we will use the definition reported in equation (1) as our goal is to extend the fuzzyQSBA algorithm [34].…”

Section: Fuzzy Subsethood Measuresmentioning

confidence: 99%

A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. Methods and materials:In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1,000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches were used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1,073 remain unclassified, representing a more clinically usable class assignment algorithm. Conclusion:The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.

show abstract

Section: Fuzzy Subsethood Measuresmentioning

confidence: 99%

A quantifier-based fuzzy classification system for breast cancer patients

Soria

Garibaldi

Green

et al. 2013

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…On the basis of the definition of similarity measure proposed by Wang 26 , the definition of VYsubsethood measure 30 , * -subsethood measure 11 and DI-subsethood measure 4 , and the definition of fuzzy entropy introduced by De Luca and Termini 9 , we have investigated the relations among similarity measure, subsethood measure and fuzzy entropy. We also have presented several propositions that similarity measure, subsethood measure and fuzzy entropy can be transformed by each other based on their axiomatic definitions.…”

Section: Discussionmentioning

confidence: 99%

“…Thus every VY-subsethood measure is also a * -subsethood measure. In 2006, Bustince et al 4 modified two of Young's axioms and proposed a new class of subsethood measure called DI-subsethood measure.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

See 1 more Smart Citation

Relations among similarity measure, subsethood measure and fuzzy entropy

Qin

2013

IJCIS

View full text Add to dashboard Cite

show abstract

“…The concept of fuzzy boundary was introduced in the framework of fuzzy edge detection problems [11,12,13]. But it should be emphasized that the output of a fuzzy edge detection problem does not produce a suitable fuzzy image segmentation output.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy edge-based image segmentation approach

Guada¹,

Gómez²,

Rodríguez³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

View full text Add to dashboard Cite

In this paper, we present a way to define the concept of fuzzy image segmentation, which has not been clearly defined in the literature. In this work the term Fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which can be understood as the fuzzy boundary of the image. Also we discuss a visualization of an image segmentation in terms of edge detection. But first, we define two concepts of crisp image segmentation on an image network, one based on nodes and the other one focusing on edges.

show abstract

Definition and construction of fuzzy DI-subsethood measures

Cited by 113 publications

References 45 publications

A quantifier-based fuzzy classification system for breast cancer patients

A quantifier-based fuzzy classification system for breast cancer patients

Relations among similarity measure, subsethood measure and fuzzy entropy

A fuzzy edge-based image segmentation approach

Contact Info

Product

Resources

About