Applying Fuzzy Possibilistic Methods on Critical Objects

Hossein Yazdani; Daniel Ortiz-Arroyo; Kazimierz Choros; Halina Kwasnicka

doi:10.1109/CINTI.2016.7846417

Applying Fuzzy Possibilistic Methods on Critical Objects

Hossein Yazdani, Daniel Ortiz-Arroyo, Kazimierz Choros, Halina Kwasnicka

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

7 Citations (Scopus)

361 Downloads (Pure)

Abstract

Providing a ﬂexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is
evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a speciﬁc cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneﬁcial.

Original language	English
Title of host publication	Proceedings of IEEE 17th International Symposium on Computational Intelligence and Informatics
Number of pages	6
Publisher	IEEE Press
Publication date	Nov 2016
Pages	271-276
ISBN (Print)	978-1-5090-3909-8/16
DOIs	https://doi.org/10.1109/CINTI.2016.7846417
Publication status	Published - Nov 2016
Event	IEEE 17th International Symposium on Computational Intelligence and Informatics - Budapest, Hungary Duration: 17 Nov 2016 → 19 Nov 2016 http://conf.uni-obuda.hu/cinti2016/

Conference

Conference	IEEE 17th International Symposium on Computational Intelligence and Informatics
Country/Territory	Hungary
City	Budapest
Period	17/11/2016 → 19/11/2016
Internet address	http://conf.uni-obuda.hu/cinti2016/

Keywords

Data Object
Critical Objects
Fuzzy Possibilistic Method
Classification
Clustering
Membership Function

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10.1109/CINTI.2016.7846417

Comparing Fuzzy Possibilistic Methods on Critical Objects_Daniel_Ortiz_ArroyoAccepted author manuscript, 817 KB

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title = "Applying Fuzzy Possibilistic Methods on Critical Objects",

abstract = "Providing a ﬂexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects isevaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a speciﬁc cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneﬁcial.",

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booktitle = "Proceedings of IEEE 17th International Symposium on Computational Intelligence and Informatics",

publisher = "IEEE Press",

note = "IEEE 17th International Symposium on Computational Intelligence and Informatics, CINTI 2016 ; Conference date: 17-11-2016 Through 19-11-2016",

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Yazdani, H, Ortiz-Arroyo, D, Choros, K & Kwasnicka, H 2016, Applying Fuzzy Possibilistic Methods on Critical Objects. in Proceedings of IEEE 17th International Symposium on Computational Intelligence and Informatics. IEEE Press, pp. 271-276, IEEE 17th International Symposium on Computational Intelligence and Informatics, Budapest, Hungary, 17/11/2016. https://doi.org/10.1109/CINTI.2016.7846417

Applying Fuzzy Possibilistic Methods on Critical Objects. / Yazdani, Hossein; Ortiz-Arroyo, Daniel; Choros, Kazimierz et al.
Proceedings of IEEE 17th International Symposium on Computational Intelligence and Informatics. IEEE Press, 2016. p. 271-276.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Applying Fuzzy Possibilistic Methods on Critical Objects

AU - Yazdani, Hossein

AU - Ortiz-Arroyo, Daniel

AU - Choros, Kazimierz

AU - Kwasnicka, Halina

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AB - Providing a ﬂexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects isevaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a speciﬁc cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneﬁcial.

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KW - Clustering

KW - Membership Function

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PB - IEEE Press

T2 - IEEE 17th International Symposium on Computational Intelligence and Informatics

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ER -

Applying Fuzzy Possibilistic Methods on Critical Objects

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