Modified Average of the Base-Level Models in the Hill-Climbing Bagged Ensemble Selection Algorithm for Credit Scoring

Handhika, Tri; Fahrurozi, Achmad; Zen, Revaldo Ilfestra Metzi; Lestari, Dewi; Sari, Ilmiyati; Murni, Murni

doi:10.1016/j.procs.2019.08.162

Cited by 7 publications

(1 citation statement)

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“…The ensemble classification system is based on Radial Basis Function Neural Network (RBFN), Decision Tree (DT), Multi-layer Feed Forward Neural Networks (MLFN), Naive Bayes (NB) and Probabilistic Neural Networks (PNN) [25]. The Hybrid technique where Multivariate Adaptive Regression Splines (MARS) were applied for features selection while ensemble classifier for classification with German credit data [26]. A proposed modification of the Gustafson-Kessel algorithm of credit risk valuation was Sammeer Iraqi Journal of Science, 2023, Vol.…”

Section: The Related Workmentioning

confidence: 99%

Modified Multi-Criteria Decision Making Methods to Assess Classification Methods

Sameer

2023

Iraqi Journal of Science

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During the last few decades, many academic and professional groups gave attention to adopting the multi-criteria decision-making methods in a variety of contexts for decision-making that are given to the diversity and sophistication of their selections. Five different classification methods are tested and assessed in this paper. Each has its own set of five attribute selection approaches. By using the multi-criteria decision-making procedures, these data can be used to rate options. Technique for order of preference by similarity to ideal solution (TOPSIS) is designed utilizing a modified fuzzy analytic hierarchy process (MFAHP) to compute the weight alternatives for TOPSIS in order to obtain the confidence value of each classifier for each feature selection approach individually. Defuzzification of the fuzzy values to obtain the final criteria weights, the rank function is used. The modification of TOPSIS is assessed in tests using five prediction models (alternatives) and six performance measurements (criteria) to analyze the German credit data sets. Overall the results of the experiment show that the proposed strategies are successful in credit approval data.

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Section: The Related Workmentioning

confidence: 99%