142 research outputs found
Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models
No full textFuzzy-Rough Cognitive Networks (FRCNs) are recurrent neural networks intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its transparency and simplicity while being competitive to state-of-the-art classifiers. Despite of their relative empirical success in terms of prediction rates, there are limited studies on FRCNs' dynamic properties and how their building blocks contribute to algorithm's performance. In this paper, we theoretically study these issues and conclude that boundary and negative neurons always converge to a unique fixed-point attractor. Moreover, we demonstrate that negative neurons have no impact on algorithm's performance and that the ranking of positive neurons is invariant. Moved by our theoretical findings, we propose two simpler fuzzy-rough classifiers that overcome the detected issues and maintain the competitive prediction rates of this classifier. Toward the end, we present a case study concerned with image classification in which a Convolutional Neural Network is coupled with one of the simpler models derived from the theoretical analysis of the FRCN model. The numerical simulations suggest that, once the features have been extracted, our granular neural system performs as well as other recurrent neural networks.The authors would like to thank the anonymous reviewers for their valuable and constructive feedback
Anuário Científico – 2008 Resumos de Artigos, Comunicações, Teses e Livros
Full text linkA divulgação do conhecimento resultante da Ciência, Investigação e
Actividade Profissional de mérito reconhecido são indissociáveis e
necessários numa sociedade em evolução, sem descurar a vertente
pedagógica, numa Instituição de Ensino Superior.
Verificou-se que durante este período se assistiu a um incremento das
publicações científicas dos docentes do ISEL. Por outro lado, existiu
um maior envolvimento em projectos de investigação e um acréscimo
na conclusão do grau de Doutor.
Assim, o anuário científico de 2008 constitui um documento de divulgação
desta actividade no Instituto Superior de Engenharia de Lisboa
em parceria com outros Politécnicos, Universidades e Centros de
Investigação nacionais e internacionais.
Numa altura em que se avizinham mudanças estruturais no Ensino
Superior, esperamos que o poder político avalie as instituições pelo
trabalho desenvolvido e pela qualidade dos engenheiros que estas
formam
Estimating the limit state space of quasi-nonlinear Fuzzy Cognitive Maps
Full text linkQuasi-Nonlinear Fuzzy Cognitive Maps (q-FCMs) generalize the classic Fuzzy Cognitive Maps (FCMs) by incorporating a nonlinearity coefficient that is related to the model's convergence. While q-FCMs can be configured to avoid unique fixed-point attractors, there is still limited knowledge of their dynamic behavior. In this paper, we propose two iterative, mathematically-driven algorithms that allow estimating the limit state space of any q-FCM model. These algorithms produce accurate lower and upper bounds for the activation values of neural concepts in each iteration without using any information about the initial conditions. As a result, we can determine which activation values will never be produced by a neural concept regardless of the initial conditions used to perform the simulations. In addition, these algorithms could help determine whether a classic FCM model will converge to a unique fixed-point attractor. As a second contribution, we demonstrate that the covering of neural concepts decreases as the nonlinearity coefficient approaches its maximal value. However, large covering values do not necessarily translate into better approximation capabilities, especially in the case of nonlinear problems. This finding points to a trade-off between the model's nonlinearity and the number of reachable states.</p
Recommended from our members
FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps
No full textFuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characte..
Recommender system using Long-term Cognitive Networks
No full textIn this paper, we build a recommender system based on Long-term Cognitive Networks (LTCNs), which are a type of recurrent neural network that allows reasoning with prior knowledge structures. Given that our approach is context-free and that we did not involve human experts in our study, the prior knowledge is replaced with Pearson’s correlation coefficients. The proposed architecture expands the LTCN model by adding Gaussian kernel neurons that compute estimates for the missing ratings. These neurons feed the recurrent structure that corrects the estimates and makes the predictions. Moreover, we present an extension of the non-synaptic backpropagation algorithm to compute the proper non-linearity of each neuron together with its activation boundaries. Numerical results using several case studies have shown that our proposal outperforms most state-of-the-art methods. Towards the end, we explain how can we inject expert knowledge to the proposed neural system
Learning of Fuzzy Cognitive Map models without training data
No full textThis paper introduces a novel zero-data learning algorithm tailored for Fuzzy Cognitive Map (FCM) models utilized in control applications where we must maintain concepts’ activation values within predefined intervals. Our approach allows domain experts to specify these intervals and optionally impose weight constraints, ensuring the algorithm produces feasible models. At the core of our approach lies a mathematical formalism that approximates the smallest feasible activation space for each neural concept, which translates into lower and upper bounds for concepts’ activation values. Moreover, a parameterized quasi-nonlinear reasoning rule allows controlling whether or not the network converges to a unique fixed point. The learning goal of our algorithm narrows down to computing a weight matrix minimizing the error between the analytical bounds and the target intervals specified by domain experts. To address such a constrained minimization problem, we employ numerical methods operating with approximate gradients, which provide highly accurate solutions with short execution times. The main contribution of our learning algorithm is that it does not require any training data to compute the network structure. Therefore, by accurately approximating the specified activation intervals, our learning algorithm guarantees that the outputs produced by the FCM model will remain within these intervals regardless of the initial conditions used to start the recurrent reasoning process
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps
Full text linkIn this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded
Learning-based aggregation of Quasi-Nonlinear Fuzzy Cognitive Maps
Full text linkQuasi-Nonlinear Fuzzy Cognitive Maps (q-FCMs) are an algorithmic generalization of Fuzzy Cognitive Maps (FCMs) used for modeling and simulation. The key advantages of q-FCMs include their interpretability and hybrid reasoning capabilities where expert knowledge and historical data can be exploited to build the model. Another distinctive feature of neural cognitive mapping is that it allows the aggregation of different models that represent the same problem into a unified neural system. Unfortunately, existing aggregation algorithms focus on producing an aggregated model that resembles the structure of the individual q-FCMs, while neglecting the functional aspect. The ramification of this oversight is that the simulation results produced by the aggregated model often differ significantly from those generated by the individual models. In this paper, we introduce a parameterized learning-based method for aggregating q-FCMs that considers both aspects. Firstly, it ensures that the aggregated model’s weight matrix is reasonably similar to those associated with the individual models, thus maintaining the structural integrity of the aggregation. Secondly, it ensures that the aggregated model’s outputs closely align with those produced by the individual models when operating under the same initial conditions. The core of our aggregation method lies in an analytically derived loss function that is minimized using a gradient-based optimizer which approximates the Jacobian and Hessian while using a limited amount of memory. Extensive simulations on synthetically generated models and a case study with diverse structural properties and complexities demonstrate that our approach significantly outperforms representative state-of-the-art methods
A computational tool for simulation and learning of Fuzzy Cognitive Maps
No full textDuring the last decade Fuzzy Cognitive Maps (FCM) have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields
The Dynamics of Trust in XAI: Assessing Perceived and Demonstrated Trust Across Interaction Modes and Risk Treatments
Full text linkThe increasing use of artificial intelligence (AI) models across various fields has raised concerns about whether these models can meet user trust expectations. As a result, researchers are focusing on assessing AI models’ performance relative to user expectations to determine trust levels. Evidence suggests that effective interaction with eXplainable AI (XAI) techniques can mitigate over-reliance on AI models and better align user expectations with the actual capabilities of these models in decision-making. In this study, we analyze trust from two perspectives: perceived trust, based on user self-reported trust, and demonstrated trust, which evaluates whether users, when given a choice, prefer to rely on AI or make decisions independently. We also explore how different interactions between human subjects and XAI models, along with varying levels of task risk, influence trust. Our findings reveal that these two types of trust are substantially different; human subjects do not always exhibit trust behavior in actual decision-making tasks, even when they perceive themselves as trusting the AI. Furthermore, we show that an AI model’s low error rate in making correct decisions can influence human subjects’ mental models, leading them to report a higher tendency to trust the AI. Finally, we conclude that human perceptions of trust are fragile and may change based on ongoing interactions with the model
- …
