Identifying Relevant Predictor Variables for a Credit Scoring Model using Compromised-Analytic Hierarchy Process (Compromised-AHP)

Eddy, Yosi Lizar; Nazri, Engku Muhammad; Mahat, Nor Idayu

doi:10.37934/arbms.20.1.113

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…More specifically, we can see the implementation for optimizing donation in healthcare, such as organ, blood, or medicine donation. There are several other methods for decision analysis such as multi-criteria decision analysis, compromised Analytic Hierarchy Process, and artificial neural network predictive model [21][22][23]. To the best of our knowledge, this is the first study for optimizing smartphone donation using the decision tree technique.…”

Section: Literature Review: Decision Tree Analysismentioning

confidence: 99%

Predicting Willingness to Donate Smartphones as a Reuse Option using Decision Tree Analysis

Gan

Halim

Aysia

et al. 2023

ARASET

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The increasing number of smartphone users in Indonesia, coupled with the short average usage time of smartphones, has posed a sustainability problem. Smartphone production requires natural resources including rare earth materials and energy, that need to be preserved due to their limited availability and depletion. Circular economy initiatives have become important in the last few decades, and one of them is extending product life by reusing discarded products. On the other hand, triggered by the Covid-19 pandemic, online learning has become an essential part of education. In Indonesia, marginalized communities or low-income societies have limitations in providing online learning tools such as desktop or laptop computers, including smartphones. Therefore, smartphone donation is considered a suitable initiative to help students who need a smartphone to join online learning while at the same time extending the useful life of a smartphone. Using decision tree techniques, we investigate the willingness to donate used smartphones for education in the Indonesian context. The results show that the respondents' willingness-to-donate was influenced by several considerations, which are performance, value (price), type, obsolescence of the used smartphone, and the donators’ age. The resulting parameters and predictions could enhance the effectiveness of the donation mechanism.

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Section: Literature Review: Decision Tree Analysismentioning

confidence: 99%

Predicting Willingness to Donate Smartphones as a Reuse Option using Decision Tree Analysis

Gan

Halim

Aysia

et al. 2023

ARASET

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“…Chu et al in [ 19 ] describe and compare 18 PDMs, although some authors suggest that only 15 are distinctive. Undoubtedly, several other PDMs have appeared in the literature since Chu et al [ 19 ] published their study; see, for example, [ 29 , 32 , 33 , 34 , 93 , 94 , 95 ]. Obviously, if the PCM is consistent, then all known PDMs match, although they do not guarantee that the resulting PV is error-free; see, e.g., [ 12 , 45 , 70 , 96 ].…”

Section: Preliminaries About Pcb Ranking Problemsmentioning

confidence: 99%

On the Statistical Discrepancy and Affinity of Priority Vector Heuristics in Pairwise-Comparison-Based Methods

Kazibudzki

2021

Entropy

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There are numerous priority deriving methods (PDMs) for pairwise-comparison-based (PCB) problems. They are often examined within the Analytic Hierarchy Process (AHP), which applies the Principal Right Eigenvalue Method (PREV) in the process of prioritizing alternatives. It is known that when decision makers (DMs) are consistent with their preferences when making evaluations concerning various decision options, all available PDMs result in the same priority vector (PV). However, when the evaluations of DMs are inconsistent and their preferences concerning alternative solutions to a particular problem are not transitive (cardinally), the outcomes are often different. This research study examines selected PDMs in relation to their ranking credibility, which is assessed by relevant statistical measures. These measures determine the approximation quality of the selected PDMs. The examined estimates refer to the inconsistency of various Pairwise Comparison Matrices (PCMs)—i.e., W = (wij), wij > 0, where i, j = 1,…, n—which are obtained during the pairwise comparison simulation process examined with the application of Wolfram’s Mathematica Software. Thus, theoretical considerations are accompanied by Monte Carlo simulations that apply various scenarios for the PCM perturbation process and are designed for hypothetical three-level AHP frameworks. The examination results show the similarities and discrepancies among the examined PDMs from the perspective of their quality, which enriches the state of knowledge about the examined PCB prioritization methodology and provides further prospective opportunities.

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Application of Machine Learning Algorithms for Creating a Wilful Defaulter Prediction Model

Maheswari¹,

Chandran²,

Sujatha³

et al. 2022

Lecture Notes in Networks and Systems

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Identifying Relevant Predictor Variables for a Credit Scoring Model using Compromised-Analytic Hierarchy Process (Compromised-AHP)

Cited by 3 publications

References 51 publications

Predicting Willingness to Donate Smartphones as a Reuse Option using Decision Tree Analysis

Predicting Willingness to Donate Smartphones as a Reuse Option using Decision Tree Analysis

On the Statistical Discrepancy and Affinity of Priority Vector Heuristics in Pairwise-Comparison-Based Methods

Application of Machine Learning Algorithms for Creating a Wilful Defaulter Prediction Model

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