Fuzzy sets

Kuncheva, Ludmila I.

doi:10.1007/978-3-7908-1850-5_4

Cited by 9 publications

(9 citation statements)

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“…Another approach that can also incorporate the “undefined” class is fuzzy classification [ 19 ]. Observations with low membership values with each class can be labeled as “undefined”.…”

Section: Resultsmentioning

confidence: 99%

Decision Confidence Assessment in Multi-Class Classification

Bukowski

Kurek

Antoniuk

et al. 2021

Sensors

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This paper presents a novel approach to the assessment of decision confidence when multi-class recognition is concerned. When many classification problems are considered, while eliminating human interaction with the system might be one goal, it is not the only possible option—lessening the workload of human experts can also bring huge improvement to the production process. The presented approach focuses on providing a tool that will significantly decrease the amount of work that the human expert needs to conduct while evaluating different samples. Instead of hard classification, which assigns a single label to each class, the described solution focuses on evaluating each case in terms of decision confidence—checking how sure the classifier is in the case of the currently processed example, and deciding if the final classification should be performed, or if the sample should instead be manually evaluated by a human expert. The method can be easily adjusted to any number of classes. It can also focus either on the classification accuracy or coverage of the used dataset, depending on user preferences. Different confidence functions are evaluated in that aspect. The results obtained during experiments meet the initial criteria, providing an acceptable quality for the final solution.

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Section: Resultsmentioning

confidence: 99%

Decision Confidence Assessment in Multi-Class Classification

Bukowski

Kurek

Antoniuk

et al. 2021

Sensors

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“…Other studies use EEG signal as the error signal underlying mechanisms of the human error processing [ 49 ]. In [ 50 ], the system performance is the indicator for the reward calculation. In this study, authors introduced deep reinforcement Q-learning to study the correlation between drowsiness and driving performance.…”

Section: Current Status Problem Statement and The Proposed Solutionmentioning

confidence: 99%

“…Usually RL measures future reward to assess the current action. The specific of the proposed Q-learning in [ 50 ] is that the reward (in terms of RT) is measured in some latency. However, this brings elements of supervised learning, such as the transition weight beta and history-dependent prediction.…”

Section: Current Status Problem Statement and The Proposed Solutionmentioning

confidence: 99%

A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations

Lekova

Chavdarov

2021

Computational Intelligence and Neuroscience

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Researchers in neuroscience computing experience difficulties when they try to carry out neuroanalysis in practice or when they need to design an explainable brain-computer interface (BCI) with quick setup and minimal training phase. There is a need of interpretable computational intelligence techniques and new brain states decoding for more understandable interpretation of the sensory, cognitive, and motor brain processing. We propose a general-purpose fuzzy software system shell for developing a custom EEG BCI system. It relies on the bursts of the ongoing EEG frequency power synchronization/desynchronization at scalp level and supports quick BCI setup by linguistic features, ad hoc fuzzy membership construction, explainable IF-THEN rules, and the concept of the Internet of Things (IoT), which makes the BCI system device and service independent. It has a potential for designing both passive and event-related BCIs with options for visual representation at scalp-source level in response to time. The feasibility of the proposed system has been proven by real experiments and bursts for β and γ frequency power have been detected in real time in response to evoked visuospatial selective attention. The presence of the proposed new brain state decoding can be used as a feasible metric for interpretation of the spatiotemporal dynamics of the passive or evoked neural oscillations.

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“…Zadeh was well known for proposing a fuzzy system in 1965 [8]. This system was developed using approximate reasoning to find its solution, neither very imprecise nor very precise [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted

Hakim

Muwardi

Yunita

et al. 2022

IJEECS

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Fuzzy Mamdani knows as one of the modern control systems. It was known to have a better performance result when compared to conventional methods. However, because the input of this modern control system sometimes is based on human experience, therefore its performance is sometimes below the conventional one. We propose using the adaptive neuro fuzzy inference system assisted (ANFIS) approach to optimize the fuzzy Mamdani membership function input to overcome this problem. We have tested our hypotheses in water chiller applications based on microcontrollers. Even though it is still behind conventional methods to cool 200 ml water, which is 6 minutes, using fuzzy ANFIS methods, we manage to improve the speed performance in cooling water from 20 minutes to 17 minutes, which is from room temperature to just 24 <sup>o</sup>C.

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Fuzzy sets

Cited by 9 publications

References 0 publications

Decision Confidence Assessment in Multi-Class Classification

Decision Confidence Assessment in Multi-Class Classification

A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations

Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted

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