Fuzzy Version of Gini’s Index

Belhadj, Besma; Kaabi, Firas; Bouanani, Mejda

doi:10.1007/s11205-021-02681-z

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Yadav and Pal [7] combined pearson correlation and lasso regularization with random forest to achieve 99% accuracy with the heart disease dataset. Belhadj et al [8] proposed a fuzzy version of gini index to improve the performance of gini index. A diverse range of research studies has been conducted around coronary illness.…”

Section: Methodsmentioning

confidence: 99%

Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Zeffora

Shobarani

2022

IJEECS

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Random <span>forest is an ensemble algorithm for machine learning. In decision trees, the splitting criteria is built on the prediction of the nodal points and formation of rules by Gini index and Information Gain. Gini index is a measure of inequality. Gini index does not take into consideration the structural changes in the dataset, and inaccurate data can distort the validity of the gini-coefficient. For data with the same feature but different outcomes, the gini-coefficient remained the same. The proposed method for attribute selection measure takes into consideration that there may be structural changes in the dataset overtime and it adapts to such expected changes and maintain the accuracy of the algorithm avoiding under-fitting and over-fitting. A dataset on myocardial infarctions was taken for the study and the results were promising.</span>

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Section: Methodsmentioning

confidence: 99%

Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Zeffora

Shobarani

2022

IJEECS

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“…In the next section, an alternative approach to calculate Gini coefficient is proposed considering the differential convexity of the Lorenz curve. It's about a fuzzy version of the Gini coefficient calculated as agreement index for fuzzy numbers (see Belhadj et al (2021) for another proposal). Fuzzy version of the Gini coefficient is a measure of inequality.…”

Section: Traditional Gini's Indexmentioning

confidence: 99%

Attributes inequality in multidimensional poverty measures fuzzy modeling

Belhadj

Bouanani

2022

Soft Comput

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Poverty is recently considered to be a multidimensional one. That is to say the poor can suffer multiple disadvantages at the same time. This paper aims to further develop and refine the multidimensional poverty measure using Fuzzy Sets Theory (FST). The application of FST starts with the choice of membership functions and the rules to manipulate to integrate attributes inequality in multidimensional poverty measure. An application based on individual well-being data from Tunisian households in 2015 is presented to illustrate use proposed concepts.

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Fuzzy Version of Gini’s Index

Cited by 2 publications

References 4 publications

Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Attributes inequality in multidimensional poverty measures fuzzy modeling

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