Towards More Specific Estimation of Membership Functions for Data-Driven Fuzzy Inference Systems

Fuchs, Caro; Wilbik, Anna; Kaymak, Uzay

doi:10.1109/fuzz-ieee.2018.8491524

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…The porting of FUMOSO [25] to Simpful, currently in progress, will also promote the use of DFMs for the investigation of complex systems. Simpful is also employed within pyFUME [43], a novel Python package developed to estimate FISs automatically from data [44][45][46][47]. Moreover, Simpful can be readily integrated in computational intelligence methods that use Mamdani or Takagi-Sugeno inference, such as the class of global optimization meta-heuristics exploiting fuzzy reasoning for dynamic parameter adaptation [48,49].…”

Section: Discussionmentioning

confidence: 99%

Simpful: A User-Friendly Python Library for Fuzzy Logic

Spolaor¹,

Fuchs²,

Cazzaniga³

et al. 2020

IJCIS

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Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.

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Section: Discussionmentioning

confidence: 99%

Simpful: A User-Friendly Python Library for Fuzzy Logic

Spolaor¹,

Fuchs²,

Cazzaniga³

et al. 2020

IJCIS

Self Cite

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“…Concerning their transparency, these models are labeled by the authors as "(light) grey box" models: on one side, in fact, linguistic fuzzy rules are easily comprehensible to human beings; on the other side, however, the procedure adopted for the estimation of the antecedent parameters substantially undermines the interpretability of the whole system. Such a procedure, described in [6], exploits clustering for partitioning data in the input-output product space and estimates antecedent parameters by fitting the convex envelop of the projected membership values for each discovered cluster. Compared to the traditional clustering-based approach [7], the procedure implemented in PyFUME pursues more specific membership functions (through the removal of outlying cluster membership values), but it still exhibits the following problem: inevitably, the estimated membership functions will not meet the criteria, generally deemed crucial for interpretability of FRBSs [8], of coverage, completeness, distinguishability and complementarity, as they are automatically derived from data.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we show that this approach allows achieving performance similar to state-of-the-art approaches, regardless of the federated setting. We adopt the recently delivered PyFUME implementation of TSK-FRBS [5], [6] as comparison. We recall the two main differences among our approach and the PyFUME version:…”

Section: Comparison With a State-of-the-art Approachmentioning

confidence: 99%

An Approach to Federated Learning of Explainable Fuzzy Regression Models

Bárcena

Ducange

Ercolani

et al. 2022

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Federated Learning (FL) has been proposed as a privacy preserving paradigm for collaboratively training AI models: in an FL scenario data owners learn a shared model by aggregating locally-computed partial models, with no need to share their raw data with other parties. Although FL is today extensively studied, a few works have discussed federated approaches to generate explainable AI (XAI) models. In this context, we propose an FL approach to learn Takagi-Sugeno-Kang Fuzzy Rule-based Systems (TSK-FRBSs), which can be considered as XAI models in regression problems. In particular, a number of independent data owner nodes participate in the learning process, where each of them generates its own local TSK-FRBS by exploiting an ad-hoc defined procedure. Then, these models are forwarded to a server that is responsible for aggregating them and generating a global TSK-FRBS, which is sent back to the nodes. An appropriate aggregation strategy is proposed to preserve the explainability of the global TSK-FRBS.A thorough experimental analysis highlights that the proposed approach brings benefits, in terms of accuracy, to data owners participating in the federation preserving the privacy of the data. Indeed, the accuracy achieved by the global TSK-FRBS is higher than the ones of the TSK-FRBSs learned by exploiting only local training data.

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“…Currently, pyFUME offers facilities to simplify the following operations: loading of the input data, with automatic partitioning between training and test data sets; clustering of the data in the input-output space by means of Fuzzy C-Means (FCM) clustering [32] or the method based on Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO [24,25]) described in the previous sections; estimation of the antecedent sets of the fuzzy model, using the method described in [38], using Gaussian (default option), double Gaussian, or sigmoidal membership functions; estimation of the consequent parameters of the first-order TS fuzzy model, implementing the functionalities described in [39]; generation, using the estimated antecedents and consequents, of an executable Simpful model (with the possibility of exporting the source code as a separate, executable file). pyFUME also provides a facility for the testing of the derived model, providing functionalities for the measurement of Root Mean Square Error (RMSE), Mean Square Error (MSE), or Mean Absolute Error (MAE).…”

Section: Simpfulmentioning

confidence: 99%

Tremor assessment using smartphone sensor data and fuzzy reasoning

et al. 2021

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Background Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments. Methods We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient’s intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules. Results Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78–81% compared to linear models and by 71–74% compared to a model based on decision trees. Conclusion This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

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Towards More Specific Estimation of Membership Functions for Data-Driven Fuzzy Inference Systems

Cited by 12 publications

References 27 publications

Simpful: A User-Friendly Python Library for Fuzzy Logic

Simpful: A User-Friendly Python Library for Fuzzy Logic

An Approach to Federated Learning of Explainable Fuzzy Regression Models

Tremor assessment using smartphone sensor data and fuzzy reasoning

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