A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach

• URL: http://arxiv.org/abs/2010.11720v1
• Date: Wed, 21 Oct 2020 14:37:02 GMT
• Title: A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach
• Authors: Betania S. C. Campello, Leonardo T. Duarte, Jo\~ao M. T. Romano
• Abstract summary: We propose a new approach to rank the alternatives based on the criteria time-series features (tendency, variance, etc.) In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the textittensors. Computational results reveal that it is possible to rank the alternatives from a new perspective by considering meaningful decision-making information.
• Score: 1.3750624267664155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the criteria' time series since providing essential information for decision-making (e.g., an improvement of the criteria). To deal with this issue, we propose a new approach to rank the alternatives based on the criteria time-series features (tendency, variance, etc.). In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the \textit{tensors}, instead of the classical matrix representation used in MCDA. Consequently, we propose an extension for the TOPSIS method to handle a tensor rather than a matrix. Computational results reveal that it is possible to rank the alternatives from a new perspective by considering meaningful decision-making information.

Related papers

• 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)
• Multicriteria decision support employing adaptive prediction in a tensor-based feature representation [8.333246626497361]
  Multicriteria decision analysis is a widely used tool to support decisions in which a set of alternatives should be ranked or classified. Recent studies have shown the relevance of considering not only current evaluations of each criterion but also past data. This study deals with this challenge via essential tools of signal processing, such as tensorial representations and adaptive prediction.
  arXiv  Detail & Related papers  (2024-01-12T19:46:29Z)
• A new fuzzy multi-attribute group decision-making method based on TOPSIS and optimization models [3.697049647195136]
  A new method is proposed for multi-attribute group decision-making in interval-valued intuitionistic fuzzy sets. By minimizing the sum of differences between individual evaluations and the overallconsistent evaluations of all experts, a new optimization model is established for determining expert weights. The complete fuzzy multi-attribute group decision-making algorithm is formulated, which can give full play to the advantages of subjective and objective weighting methods.
  arXiv  Detail & Related papers  (2023-11-27T15:41:30Z)
• Domain Generalization via Rationale Invariance [70.32415695574555]
  This paper offers a new perspective to ease the challenge of domain generalization, which involves maintaining robust results even in unseen environments. We propose treating the element-wise contributions to the final results as the rationale for making a decision and representing the rationale for each sample as a matrix. Our experiments demonstrate that the proposed approach achieves competitive results across various datasets, despite its simplicity.
  arXiv  Detail & Related papers  (2023-08-22T03:31:40Z)
• Fast Feature Selection with Fairness Constraints [49.142308856826396]
  We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants. We extend the adaptive query model, recently proposed for the greedy forward selection for submodular functions, to the faster paradigm of Orthogonal Matching Pursuit for non-submodular functions. The proposed algorithm achieves exponentially fast parallel run time in the adaptive query model, scaling much better than prior work.
  arXiv  Detail & Related papers  (2022-02-28T12:26:47Z)
• Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment [79.5678820246642]
  We show that certain action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions. We generalize the recently proposed societal decision-making framework as a more granular formalism than the Markov decision process.
  arXiv  Detail & Related papers  (2021-06-28T21:29:13Z)
• 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)
• Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models [0.0]
  We propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness.
  arXiv  Detail & Related papers  (2020-12-01T06:34:48Z)
• Multi-Task Multicriteria Hyperparameter Optimization [77.34726150561087]
  The article begins with a mathematical formulation of the problem of choosing optimal hyperparameters. The steps of the MTMC method that solves this problem are described. The proposed method is evaluated on the image classification problem using a convolutional neural network.
  arXiv  Detail & Related papers  (2020-02-15T12:47:53Z)
• Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems [8.637110868126546]
  We propose a novel approach whose aim is to estimate, from the observed data, a set of independent latent criteria. A central element of our approach is to formulate the decision problem as a blind source separation problem. We consider TOPSIS-based approaches to obtain the ranking of alternatives from the latent criteria.
  arXiv  Detail & Related papers  (2020-02-06T13:51:28Z)

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.