Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis

• URL: http://arxiv.org/abs/2509.24996v1
• Date: Mon, 29 Sep 2025 16:21:30 GMT
• Title: Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
• Authors: Juan B. Cabral, Alvaro Roy Schachner,
• Abstract summary: Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation.<n>We present a framework that addresses this methodological uncertainty through automated exploration of the scaling transformation space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological uncertainty through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to automatically generate all possible methodological combinations and provide robust comparative analysis.

Related papers

• On the System Theoretic Offline Learning of Continuous-Time LQR with Exogenous Disturbances [3.701656361145375]
  We analyze offline designs of linear quadratic regulator (LQR) strategies with uncertain disturbances.<n>Our approach builds on the fundamental learning-based framework of adaptive dynamic programming.
  arXiv  Detail & Related papers  (2025-09-20T17:14:27Z)
• Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation [50.34670342434884]
  We propose a novel methodology for modeling posterior drift through Bayes decision rules.<n>Under mild regularity conditions, we establish the consistency of our estimators and derive the risk bounds.<n>We illustrate the broad applicability of our method by adapting it to the estimation of optimal individualized treatment rules.
  arXiv  Detail & Related papers  (2025-08-28T16:03:06Z)
• Dynamic Feature Selection based on Rule-based Learning for Explainable Classification with Uncertainty Quantification [0.7874708385247353]
  Dynamic feature selection (DFS) offers a compelling alternative to traditional, static feature selection.<n>DFS customizes feature selection per sample, providing insight into the decision-making process for each case.
  arXiv  Detail & Related papers  (2025-08-04T16:21:43Z)
• Algorithmic Detection of Rank Reversals, Transitivity Violations, and Decomposition Inconsistencies in Multi-Criteria Decision Analysis [0.0]
  We present three tests that detect the presence of Rank Reversals, along with their implementation in the Scikit-Criteria library.<n>We also address the complications that arise when implementing these tests for general scenarios and the design considerations we made to handle them.
  arXiv  Detail & Related papers  (2025-07-31T19:31:41Z)
• A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data [0.0]
  We propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models.<n>The VGA framework, rooted in linear programming, is pivotal in the MCA methodology.
  arXiv  Detail & Related papers  (2025-07-10T04:00:48Z)
• Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis [6.399565088857091]
  We introduce Switching Non-Stationary Markov Decision Processes (SNS-MDP), where environments switch over time based on an underlying Markov chain.<n>Under a fixed policy, the value function of an SNS-MDP admits a closed-form solution determined by the Markov chain's statistical properties.<n>We show how this framework can effectively guide decision-making in complex, time-varying contexts.
  arXiv  Detail & Related papers  (2025-03-24T12:05:30Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities [50.6382396309597]
  Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.<n>We present a complete and fair evaluation of existing shallow algorithms, including reweighting, mapping, and subspace alignment.<n>Our benchmark highlights the importance of realistic validation and provides practical guidance for real-life applications.
  arXiv  Detail & Related papers  (2024-07-16T12:52:29Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics [64.72260320446158]
  We propose a method for synthesising controllers for Markov jump linear systems. Our method is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.
  arXiv  Detail & Related papers  (2022-12-01T17:36:30Z)
• Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting [41.94295877935867]
  We present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized. We show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.
  arXiv  Detail & Related papers  (2021-02-26T02:43:28Z)
• Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach [78.05638156687343]
  We propose a methodology for analyzing POMCP policies by inspecting their traces. The proposed method explores local properties of policy behavior to identify unexpected decisions. We evaluate our approach on Tiger, a standard benchmark for POMDPs, and a real-world problem related to mobile robot navigation.
  arXiv  Detail & Related papers  (2020-12-23T15:09:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.