A Short Survey on the Usage of Choquet Integral and its Associated Fuzzy Measure in Multiple Attribute Analysis

Krishnan, Anath Rau; Kasim, Maznah Mat; Bakar, Engku Muhammad Nazri Engku Abu

doi:10.1016/j.procs.2015.07.560

Cited by 43 publications

(33 citation statements)

References 18 publications

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“…Although both of these integral methods are fuzzy and popular, Choquet fuzzy integrals have been more widely applied than Sugeno integrals [17]. A Choquet integral is an aggregation method that simultaneously considers the importance of a classifier and its interaction with other classifiers [18].…”

Section: B Fuzzy Integralmentioning

confidence: 99%

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Siami

Naderpour

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.

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Section: B Fuzzy Integralmentioning

confidence: 99%

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Siami

Naderpour

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…As a result, a respondent 1 who participated in a particular MADM analysis is required to provide 2 amount of data in the process of estimating the importance values of all possible combinations of the attributes (Bottero, 2013). Undoubtedly, this process can become a very arduous assignment, especially if the number of evaluation attributes, n involved in the analysis is sufficiently large (Kojadinovic, 2008;Krishnan et al, 2015). Many patterns of fuzzy measure have been introduced in order to reduce the complexity involved in the process of determining the general fuzzy measure values and λ-measure is one such pattern.…”

Section: Choquet Integral and -Measurementioning

confidence: 99%

An alternate method to determine λ0-measure values prior to applying Choquet integral in a multi-attribute decision making environment

Krishnan¹,

Wahab²,

Kasim³

et al. 2019

10.5267/j.dsl

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Determining 2 values of fuzzy measure prior to applying Choquet integral normally turns into a complex undertaking, especially when the decision problem entails a large number of evaluation attributes,. Many patterns of fuzzy measure have thus been suggested to deal with this complexity.-measure is one such pattern. However, the original-measure identification method was found to be unsuccessful in providing clear-cut indications on the relationships held by the attributes. A revised version of the method was then introduced to tackle this issue, but unfortunately it requires a large amount of initial data from the respondents compared to the original method. This paper therefore proposes an alternate version of-measure identification method that synchronously compensates the shortcomings associated with each existing method. The proposed method uses interpretive structural modelling (ISM) to uncover the actual relationships held by the attributes. The outputs of ISM (i.e. digraph, driving power and dependence power) are then utilised to determine the inputs required to identify the complete set of-measure values. A supplier selection problem was used to demonstrate the feasibility of the method. Also, the usability of the method was compared over the existing ones. .

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“…Unfortunately, additive operators presume that attributes are always independent to each other. This assumption is inapt with real scenario where the features hold interactive characteristics [57]. Therefore, aggregation should not be always carried out using additive operators.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In fact, the application of Choquet integral can generate more practical outcomes as most of the multiple feature problems involve numbers which have a real meaning (interval or ratio level of measurement) where cardinal aggregation is intended, unlike Sugeno integral which is more suitable for ordinal aggregation where only the order of the elements is important. Secondly, Choquet integral has the merit in producing unique solution in contrast to Sugeno integral [57]. According to a comparison study between Choquet and Sugeno integrals as aggregation operators for pattern recognition, done by Martinez and al.…”

Section: Feature Selectionmentioning

confidence: 99%

Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects

Ameur

Sellami

Wendling

et al. 2019

BDCC

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In this work, we build a computer aided diagnosis (CAD) system of breast cancer for high risk patients considering the breast imaging reporting and data system (BIRADS), mapping main expert concepts and rules. Therefore, a bag of words is built based on the ontology of breast cancer analysis. For a more reliable characterization of the lesion, a feature selection based on Choquet integral is applied aiming at discarding the irrelevant descriptors. Then, a set of well-known machine learning tools are used for semantic annotation to fill the gap between low level knowledge and expert concepts involved in the BIRADS classification. Indeed, expert rules are implicitly modeled using a set of classifiers for severity diagnosis. As a result, the feature selection gives a a better assessment of the lesion and the semantic analysis context offers an attractive frame to include external factors and meta-knowledge, as well as exploiting more than one modality. Accordingly, our CAD system is intended for diagnosis of breast cancer for high risk patients. It has been then validated based on two complementary modalities, MRI and dual energy contrast enhancement mammography (DECEDM), the proposed system leads a correct classification rate of 99%.

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A Short Survey on the Usage of Choquet Integral and its Associated Fuzzy Measure in Multiple Attribute Analysis

Cited by 43 publications

References 18 publications

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

An alternate method to determine λ0-measure values prior to applying Choquet integral in a multi-attribute decision making environment

Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects

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