Similarity classifier with ordered weighted averaging operators

Luukka, Pasi; Kurama, Onesfole

doi:10.1016/j.eswa.2012.08.014

Cited by 32 publications

(13 citation statements)

References 28 publications

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“…Combining (16) and (17) leads one to a similarity measure, which can be used to calculate the similarity between two vectors with objects. This has been earlier discussed in [50] and further applied in [11,12,21]. Thus, with the arithmetic mean, we can write the similarity between two objects 1 and 2 as…”

Section: Similarity Measuresmentioning

confidence: 99%

“…In this paper we also apply an ordered weighted averaging (OWA) based variant of the Bonferroni mean, the so-called "Bonferroni-OWA operator," proposed by Yager [5]. The basic OWA operator has previously been studied in connection with similarity classifiers in [12], but the Bonferroni-OWA operator is applied in this context for the first time. In order to effectively use the OWA operator a set of associated weights (vector of weights) is required; here we have selected using linguistic quantifiers in order to generate these weights.…”

Section: Introductionmentioning

confidence: 99%

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A Similarity Classifier with Bonferroni Mean Operators

Kurama

Luukka

Collan

2016

Advances in Fuzzy Systems

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A similarity classifier based on Bonferroni mean based operators is introduced. The new Bonferroni mean based variant of the similarity classifier is also extended to cover a new Bonferroni-OWA variant. The new Bonferroni-OWA based similarity classifier raises the question of how to accomplish the weighting needed and for this reason we also examine a number of linguistic quantifiers for weight generation. The new proposed similarity classifier variants are tested on four real world medical research related data sets. The results are compared with results from two previously presented similarity classifiers, one based on the generalized mean and another based on an arithmetic mean operator. The results show that comparatively better classification accuracy can be reached with the proposed new similarity classifier variants. Advances in Fuzzy SystemsMedical diagnosis of common diseases like breast cancer, lung cancer, hepatitis, thyroid, and many others requires high accuracy. However, in real world (medical) problems, it is most often not possible to achieve a 100% classification accuracy due to the complexity of the analyzed conditions and the complications caused by the available data [20]. The complications connected to the data can be the result of several different causes, for example, small (limited) amount of data samples that make accurate generalizations impossible, very large number of attributes and/or variables that creates complexity, and the difficulty in determining the relevance of the considered attribute. Often even small improvements in classification accuracy connected to medical diagnoses can be valuable, since even small improvements can help save human lives. Similarity based classifiers (see [21]) have been shown to have the ability to work well on medical diagnosis problems (see, e.g., [11,22]) and have advantages such as fast speed and high classification accuracy and have already been shown to work rather well with small sets of samples (see, e.g., [20]). For more information about fuzzy classification and clustering methods, see [23][24][25][26][27][28][29].The rest of the paper is organized as follows: in the second section we briefly go through the aggregation operators, the weight generation schemes for the new OWA based classifier variants, and the similarity measures applied in the paper, in the third section we introduce the new similarity classifiers and the new variants, and in the fourth section we first shortly introduce the used medical research data sets and then examine the achieved results. The paper is closed with discussion and conclusions.

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Section: Similarity Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Similarity Classifier with Bonferroni Mean Operators

Kurama

Luukka

Collan

2016

Advances in Fuzzy Systems

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“…need parameterized similarity measures in the sense of including the notion of feature importance and the precision of retrieved object. In the literature, many parameterized similarity measures are proposed [24][25] [26]. In this work we are concerned with the measure designed in [25] which is suitable with our proposed feature representation.…”

Section: B Similarity Measurementioning

confidence: 99%

A gender classification approach based on 3D depth-radial curves and fuzzy similarity based classification

Ezghari¹,

Belghini²,

Zahi³

et al. 2015

2015 Intelligent Systems and Computer Vision (ISCV)

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We propose in this paper, a gender recognition solution under the presence of occlusion and using the very restrict samples in the learning base. The developed approach is based on the extraction of pertinent 3D depth-radial curves that cover the nose region and combined dimensionality reduction using sparse random projection method; furthermore we propose an extension of similarity based classification approach to handle recognition task. Experimental results approve the effectiveness of our approach and show that the proposed method is also effective in the presence of variations such as facial expressions and rotation.

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“…The core idea of the similarity based classifier is to build ideal vectors of class representatives and use similarity in making the classification decision for the class of the sample. Similarity based classification was previously studied in several papers: different similarity measures in similarity classifiers were examined by Luukka (2007;, while aggregation with OWA operators within the similarity classifier was studied by Luukka and Kurama (2013). Similarity based classification was also found to be useful in combination with using various principal component analysis (PCA) methods (Luukka, 2009;Luukka and Leppalampi, 2006) and with feature selection (Luukka, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…This averaging operator was used for classification purposes with(in) the similarity classifier by Luukka and Kurama (2013). The OWA operator is characterized by an adjustable weighting vector.…”

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confidence: 99%

An ɴ-ary λ-averaging based similarity classifier

Kurama

Luukka

Collan

2016

International Journal of Applied Mathematics and Computer Science

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We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary norms and test the new similarity classifier with five medical data sets. The new method seems to perform well when compared with the similarity classifier.

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Similarity classifier with ordered weighted averaging operators

Cited by 32 publications

References 28 publications

A Similarity Classifier with Bonferroni Mean Operators

A Similarity Classifier with Bonferroni Mean Operators

A gender classification approach based on 3D depth-radial curves and fuzzy similarity based classification

An ɴ-ary λ-averaging based similarity classifier

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