A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure

Luukka, Pasi; Saastamoinen, Kalle; Könönen, Ville

doi:10.1109/fuzz.2001.1007281

Cited by 43 publications

(32 citation statements)

References 2 publications

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“…Compared classifiers are decision tree classifier CN2, neural network classifiers MLP and DIMLP (Bologna, 2003) and SIM which is similarity based classifier (Luukka & Leppälampi, 2006;Luukka, Saastamoinen, & Könönen, 2001). Classification results are taken from Bologna (2003) and Luukka & Leppälampi (2006) or classified for this comparison (this is done with echocardiogram data set).…”

Section: Classification Results and Comparisonmentioning

confidence: 99%

Fuzzy beans in classification

Luukka

2011

Expert Systems with Applications

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Section: Classification Results and Comparisonmentioning

confidence: 99%

Fuzzy beans in classification

Luukka

2011

Expert Systems with Applications

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“…Our motivation for this paper is to study effects of dimension reduction techniques to new fuzzy similarity measure we introduced in [2] and to improve some results what we have got from our previous research activities between fuzzy similarity in classifying [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Similarity Classifier with Generalized mean Applied to Medical Data Using Different Preprocessing Methods

Luukka

Leppälampi

2005

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.

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In this paper a study of similarity based classifier with generalized mean and different preprocessors suites for medical data sets. Also results are compared to C4.5 decision trees, multi-layer perceptrons and DIMLP which are giving quite good results. Result presented in this paper are promising. Also wider testing of classifier based on generalized mean base fuzzy similarity is carried out. The effects of PCA and entropy minimization as preprocessing methods to classification results with similarity based classifier is conducted.

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“…In this paper we present a new extension of the similarity based classifier, presented by Luukka et al (2001) and Luukka (2005). 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.…”

Section: Introductionmentioning

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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A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure

Cited by 43 publications

References 2 publications

Fuzzy beans in classification

Fuzzy beans in classification

Similarity Classifier with Generalized mean Applied to Medical Data Using Different Preprocessing Methods

An ɴ-ary λ-averaging based similarity classifier

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