Similarity classifier with generalized mean applied to medical data

Luukka, Pasi; Leppälampi, Tapio

doi:10.1016/j.compbiomed.2005.05.008

Cited by 74 publications

(35 citation statements)

References 10 publications

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“…Luukka and Leppalampi obtained 97.02% using fuzzy similarity classifier for diagnosis of erythemato-squamous diseases [7]. Polat and Gunes [8] proposed methods based on fuzzy weighted pre-processing, K-NN based weighted preprocessing, and decision tree classifier.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

Aruna¹,

Nandakishore²,

Rajagopalan³

2012

IJCA

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In this paper a diagnostic model based on Naive Bayes developed to diagnose erytemato squamous diseases. The hybrid feature selection method, named IGSBFS (Information Gain and Sequential Backward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In IGSBFS, Information Gain acts as filters to remove redundant features and SBFS with Naïve Bayes acts as the wrappers to select the ideal feature subset from the remaining features We conducted experiments in WEKA with 10 fold cross validation. The algorithm selected an optimum feature subset of 10 features with 98.9% accuracy.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

Aruna¹,

Nandakishore²,

Rajagopalan³

2012

IJCA

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“…To be precise, the new classification method is based on the extension of the lambda averaging operator presented by Klir and Yuan (1995) and on the similarity classifier proposed by Luukka and Leppalampi (2006). The new n-ary lambda operator is used in the aggregation stage of the classification, after a vector of similarities has been calculated.…”

Section: New Similarity Based N-ary Lambda Classifiermentioning

confidence: 99%

“…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). Similarity based classification was applied in a variety of classification problems, e.g., in classifying chromosomes (Sivaramakrishnan and Arun, 2014), in 3D face recognition (Ezghari et al, 2015), and in freeway * Corresponding author incident duration modeling (Vlahogianni and Karlaftis, 2013).…”

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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“…To ensure low bias, we applied the holdout method that is run 100 times as in Ref. 24. Each time data were randomly divided in two equal parts for training and testing.…”

Section: Example 2 -Similarity-based Classificationmentioning

confidence: 99%

“…Enhancing generalized Lukasiewicz structure using weights was proposed and evaluated on several well-known medical classification problems. 24 In Ref. 25, the authors introduced a similarity measure based on probabilistic equivalence relations and generalized mean.…”

Section: Introductionmentioning

confidence: 99%

IBA-based framework for modeling similarity

Milošević¹,

Poledica²,

Rakićević³

et al. 2018

IJCIS

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In this paper, we introduce a logic-driven framework for modeling similarity based on interpolative Boolean algebra (IBA). It consists of two main steps: data preprocessing and similarity measuring by means of IBA similarity measure and logical aggregation. The purpose of these steps is to detect dependencies and model interactions among attributes and/or similarities using an appropriate operator. The proposed framework is general, providing different approaches to multi-attribute object comparison: attribute-by-attribute comparison, object-level comparison and their combination. It is also a generic framework since various similarity measures can be easily derived. The proposed IBA-based similarity framework has a solid mathematical background, which ensures all necessary properties of similarity measure are satisfied. It is interpretable and close to human perception. The framework's applicability is illustrated by two numerical examples that confirm the need for a different level of aggregations. Furthermore, the example of similarity-based classification demonstrates the descriptive power and transparency of the framework on real financial data.

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Similarity classifier with generalized mean applied to medical data

Cited by 74 publications

References 10 publications

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

A Hybrid Feature Selection Method based on IGSBFS and Naive Bayes for the Diagnosis of Erythemato - Squamous Diseases

An ɴ-ary λ-averaging based similarity classifier

IBA-based framework for modeling similarity

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