scholarly journals Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 86
Author(s):  
Vladimir Stanovov ◽  
Shakhnaz Akhmedova ◽  
Yukihiro Kamiya
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Emotional State ◽  
Statistical Tests ◽  
Fuzzy Classification ◽  
High Confidence ◽  
Confidence Levels ◽  
Membership Value ◽  
Fuzzy Classification System ◽  
Difficult Cases

In this study, a new voting procedure for combining the fuzzy logic based classifiers and other classifiers called confidence-based voting is proposed. This method combines two classifiers, namely the fuzzy classification system, and for the cases when the fuzzy system returns high confidence levels, i.e., the returned membership value is large, the fuzzy system is used to perform classification, otherwise, the second classifier is applied. As a result, most of the sample is classified by the explainable and interpretable fuzzy system, and the second, more accurate, but less interpretable classifier is applied only for the most difficult cases. To show the efficiency of the proposed approach, a set of experiments is performed on test datasets, as well as two problems of estimating the person’s emotional state with the data collected by non-contact vital sensors, which use the Doppler effect. To validate the accuracies of the proposed approach, the statistical tests were used for comparison. The obtained results demonstrate the efficiency of the proposed technique, as it allows for both improving the classification accuracy and explaining the decision making process.

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

Data mining with a simulated annealing based fuzzy classification system

2008 ◽  
Vol 41 (5) ◽  
pp. 1824-1833 ◽  
Author(s):  
Hamid Mohamadi ◽  
Jafar Habibi ◽  
Mohammad Saniee Abadeh ◽  
Hamid Saadi
Keyword(s):  
Data Mining ◽  
Simulated Annealing ◽  
Classification System ◽  
Fuzzy Classification ◽  
Fuzzy Classification System

scholarly journals Building a high quality data linkage spine using a targeted approach to clerical review

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Nadine Wiggins ◽  
Brian Stokes
Keyword(s):  
Quality Assurance ◽  
Data Linkage ◽  
Quality Data ◽  
High Confidence ◽  
Software Module ◽  
High Quality ◽  
Confidence Levels ◽  
Lower Confidence ◽  
Linkage Quality

ABSTRACTObjectivesThe Tasmanian Data Linkage Unit (TDLU) was established through the University of Tasmania in 2011 with the first dataset imported to its Master Linkage Map (MLM) during 2014. Tasmania an island state of Australia, has a population of approximately 516,000. From the TDLU’s earliest inception, it was deemed important to build a high quality linkage spine comprising key administrative data representative of significant state health and related datasets to support quality population level research.ApproachThe TDLU has embraced a model of continual quality and process enhancement as a determined strategy to support ongoing business improvement. Initial linkage approaches utilised ‘traditional’ methods of reviewing record pairs within an upper and lower confidence range. This approach resulted in false record pairs with high confidence levels being linked (false positives) and true record pairs at lower confidence levels not linked (false negatives). To improve linkage quality, the TDLU has continually refined and modified its clerical review methodology with a specialist software module developed to identify specific record attributes within groups that require the group to be manually reviewed and resolved. A range of SQL queries have also been developed to identify incorrect links and further enhance the linkage quality of the MLM.ResultsThe linkage quality tools implemented have led to improved clerical review and quality assurance processes which in turn have increased the overall quality of the linkage spine. The ‘targeted’ method of clerical review provides easy identification of false positive records, particularly those with high confidence scores such as twins and husband/wife combinations. The review of groups at lower confidence levels has minimised the rate of false negative pairs however further refinement of tools is required to minimise the time spent on reviewing these groups. The clerical review software module has equipped staff with the necessary information to make informed and timely decisions when reviewing groups of records. Detailed documentation is maintained for each linkage project providing continual feedback for system and process improvements as the linkage spine increases in size.ConclusionThe process of clerical review and quality assurance requires a commitment to continual refinement of tools and techniques resulting in a higher quality linkage spine and a reduction in the total time and resource required to link datasets.

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

Observer-Based Control of a Class of Switched Fuzzy Systems

2014 ◽  
Vol 945-949 ◽  
pp. 2539-2542
Author(s):  
Hong Yang ◽  
Huan Huan Lü ◽  
Le Zhang
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Function Method ◽  
State Feedback ◽  
Closed Loop ◽  
External Disturbance ◽  
Asymptotically Stable ◽  
Closed Loop System ◽  
Lyapunov Function Method ◽  
Switching State

For the non-measurable states, a control of switched fuzzy systems is presented based on observer. Using switching technique and multiple Lyapunov function method, the fuzzy observer is built to ensure that for all allowable external disturbance the relevant closed-loop system is asymptotically stable. Moreover, switching strategy achieving system global asymptotic stability of the switched fuzzy system is given. In this model, a switching state feedback controller is presented. A simulation shows the feasibility and the effectiveness of the method.

Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains

1997 ◽  
Vol 34 (04) ◽  
pp. 859-867
Author(s):  
Béla Bollabás ◽  
Alan Stacey
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
Monte Carlo Simulations ◽  
Statistical Tests ◽  
Upper Bounds ◽  
Two Dimensions ◽  
Oriented Percolation ◽  
Critical Probability ◽  
Confidence Levels

We develop a technique for establishing statistical tests with precise confidence levels for upper bounds on the critical probability in oriented percolation. We use it to give pc < 0.647 with a 99.999967% confidence. As Monte Carlo simulations suggest that pc ≈ 0.6445, this bound is fairly tight.

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