Relative influences and the reliability of weights in fuzzy cognitive maps

Yosef, Arthur; Shnaider, Eli; Schneider, M.; Rothstein, Alexander

doi:10.1016/j.fss.2022.01.011

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Thus, developing a new method that identifies the authentic causal relations between problem variables and rules out possible spurious correlations, is considered essential [23]. In this direction, the authors in [24] presented a method for removing spurious correlations by calculating the concepts' behavioral similarity through data, and applying a set of defined rules from domain experts to discern the actual causal relationships. However, through this approach, an FCM can still contain spurious correlations that experts consider acceptable, while some actual causal associations can remain undetected as they can be beyond experts' knowledge.…”

Section: A State-of-the-art and Motivationmentioning

confidence: 99%

“…In this paper, a novel approach for FCM construction is introduced based on the causal inference tool Liang-Kleeman Information Flow (L-K IF) analysis. In more detail, in contrast to [24], the proposed technique does not require expert involvement because it identifies the actual causal relationships from the data using an automatic causal search algorithm. Finally, the derived causal links are imposed as constraints in the FCM learning procedure, aiming to rule out spurious correlations and thus improve the FCM's aggregate predictive and explanatory power.…”

Section: B Contributionmentioning

confidence: 99%

“…Regarding interpretability, the global feature importance in IF-FCM is computed using (24). As observed from its causal structure (Tab.…”

Section: B3 Performance Analysis Of If-fcmmentioning

confidence: 99%

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Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Tyrovolas¹,

Liang²,

Stylios³

2023

Preprint

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<p>A Fuzzy Cognitive Map (FCM) is a graph-based tool for knowledge representation that intends to model any complex system through an interactive structure of nodes interacting with each other through causal relationships. Owing to their flexibility and inherent interpretability, FCMs have been used in various modeling and prediction tasks, particularly in situations where humans make final decisions, such as industrial anomaly detection. However, FCMs can unintentionally absorb spurious correlations presented in collected data during development, leading to poor prediction accuracy and interpretability. To address this limitation, this article proposes a novel framework for constructing FCMs based on the Liang-Kleeman Information Flow (L-K IF) analysis, a causal inference tool. The actual causal relationships are identified from the data using an automatic causal search algorithm, and these are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the predictive and explanatory power of the model. Numerical simulations were conducted by comparing the proposed approach with state-of-the-art FCM-based models, thereby demonstrating the promising performance of the developed FCM.</p>

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Section: A State-of-the-art and Motivationmentioning

confidence: 99%

Section: B Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Tyrovolas¹,

Liang²,

Stylios³

2023

Preprint

View full text Add to dashboard Cite

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“…Such gaps provide the motivation to develop new methods that identify authentic causal relationships between problem variables and rule out possible spurious correlations (Na ´poles et al 2020b). In this direction, Yosef et al (2022) presented a method for removing spurious correlations by calculating the concepts' behavioral similarity through data and applying a set of defined rules from domain experts to discern the actual causal relationships. However, through this approach, an FCM can still contain spurious correlations that experts consider acceptable, while some actual causal associations can remain undetected as they can be beyond experts' knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…This paper's main contribution is introducing a novel approach for FCM construction, leveraging the Liang-Kleeman Information Flow (L-K IF) analysis for causal inference. In more detail, unlike the approach presented by Yosef et al (2022), the proposed technique contributes by eliminating the necessity for expert involvement; it identifies the authentic causal relationships from the data using an automatic causal search algorithm. A pivotal part of our contribution is the imposition of the derived causal links as constraints during the FCM learning procedure.…”

Section: Introductionmentioning

confidence: 99%

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Tyrovolas¹,

Liang²,

Stylios³

2023

Preprint

View full text Add to dashboard Cite

<p>Fuzzy Cognitive Maps (FCMs) is a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, one of the main limitations of FCMs is that they may unintentionally absorb spurious correlations presented in the collected data, resulting in poor prediction accuracy and interpretability. This article proposes a novel framework for constructing FCMs based on Liang-Kleeman Information Flow (L-K IF) analysis to address this limitation. The novelty of the proposed approach is the identification of actual causal relationships from the data using an automatic causal search algorithm. The actual causal relationships are then imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations were conducted to demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art FCM-based models.</p>

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Information flow-based fuzzy cognitive maps with enhanced interpretability

Tyrovolas,

Liang,

Stylios

2023

Granul. Comput.

View full text Add to dashboard Cite

Fuzzy Cognitive Maps (FCMs) are a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, a notable limitation of FCMs is their susceptibility to inadvertently capturing spurious correlations from data, undermining their prediction accuracy and interpretability. In addressing this challenge, our primary contribution is the introduction of a novel framework for constructing FCMs using the Liang-Kleeman Information Flow (L-K IF) analysis, a quantitative causality analysis rigorously derived from first principles. The novelty of the proposed approach lies in the identification of actual causal relationships from the data using an automatic causal search algorithm. These relationships are subsequently imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations validate the superiority of our method against state-of-the-art FCM-based models, thereby bolstering the reliability, accuracy, and interpretability of FCMs.

show abstract

Relative influences and the reliability of weights in fuzzy cognitive maps

Cited by 5 publications

References 15 publications

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Information Flow-Based Fuzzy Cognitive Maps with Enhanced Interpretability

Information flow-based fuzzy cognitive maps with enhanced interpretability

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