Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis

Jassim, Mustafa Mohammed; Ali, Manar Hassan; Elamer, Amer S.; Jaber, Mustafa Musa; Mohammed, Mohammed Qasim; Alkhayyat, Ahmed

doi:10.1109/iiceta54559.2022.9888273

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…He et al, [51] in prediction analysis, Pham et al, [52] in land slide vulnerability, Z. He et al, [53] in textile chemical processing, Kadkhodazadeh et al, [54] in climate change , Mustapha et al, [55] in breast cancer screening, Khosravi et al, [56] in flood susceptibility, Z. Wang et al, [57] in chemical engineering , García et al, [58] in credit affairs, Ali et al, [59] in landslide modeling , Pourkhodabakhsh et al, [60] in human resource management, Chowdhury et al, [61] in COVID-19 classification, Baqer et al, [62] in pollutant prediction, Jassim et al, [63] in diagnosis of adult autism, Al-Bawi et al, [64] in gully erosion. In the above mentioned combinations of MCDM and RFA, various decision making methods such as COPRAS, VIKOR, MULTIMOORA are used.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Sudha,

Edwin,

Nivetha

2023

E3S Web of Conf.

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The automobile industries across the world of this present age are streamlining the manufacture of battery electric vehicles (BEV) as a step towards creating pollution free environment. BEVs are used as an alternate strategy to alleviate the carbon emission at global level. As environmental conservation is one of the long standing sustainable 1f ?developmental goals it is the need of the hour to make a paradigm shift from fossil fuels to renewable energy sources, at the same time this also gives rise to a decision-making problem on making optimal choice of the electric vehicles. In this paper a decision making problem based on ten alternative BEVs and eleven criteria is considered from the earlier works of Faith Ecer. The new ranking method of multi-criteria decision making MCRAT(Multiple Criteria Ranking by Alternative Trace) is used together with three different criterion weight computing methods of AHP(Analytical Hierarchy Process) ,CRITIC (CRiteria Importance Through Intercriteria Correlation) & MEREC (MEthod based on the Removal Effects of Criteria). The results obtained are compared and validated using random forest machine learning algorithm. This research work conjoins multi-criteria decision making methods and machine learning algorithms to make optimal decisions on Battery electric vehicles and this integrated approach yields optimal ranking results and it will certainly create new rooms in decision-making approaches in coming days.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Sudha,

Edwin,

Nivetha

2023

E3S Web of Conf.

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Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

Giannopoulos,

Dasaklis,

Rachaniotis

2024

Sci Rep

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This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm’s training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k -parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensitivity was assessed by applying different distance metrics. By enhancing informational content, the composite data structures contribute to both accuracy improvements and scalability, particularly in data-sparse contexts. This relationship underscores the critical role of entropy in enhancing the performance of explainable machine learning algorithms, providing a valuable and interpretable tool for transforming data structures in sparse data environments.

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Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis

Cited by 2 publications

References 14 publications

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

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