Confidence Degrees Research Articles

A two-stage stochastic preference analysis on group decision making with interval additive preference relations

Interval additive preference relation (IAPR) is an efficient tool for describing the uncertain pairwise comparisons between alternatives in group decision-making (GDM). Existing GDM methods with IAPRs view the preference value as a whole and establish concrete conflict/consensus mechanisms for fusing individual opinions while neglecting the stochastic preference and aggregation modes to meet the different demands of decision. Thus, to reflect the stochastic characteristic in both preference representation and group preference aggregation, this paper presents a novel two-stage stochastic preference analysis on GDM with IAPRs. In first stage, the method involves extracting stochastic additive preference relations (SAPRs) from the IAPRs. A preference relations matrix is then established using a logarithmic least squares model applied to the SAPRs. In second stage, the weighted preference matrix is synthesized by combining the preference relations matrix with the decision makers’ weight space. This leads to the computation of positive and negative ideal preference solutions, as well as positive and negative preference distances. Subsequently, the relative preference closeness is calculated based on these distances. Relative ranking results are determined by an algorithm evaluating pairwise comparisons among alternatives, resulting in transitional preference rankings. The second stage involve deriving ranking acceptance indices and ranking confidence degrees through a continuous extraction process, facilitating the establishment of the ultimate best preference rankings. The proposed method’s effectiveness and validity are demonstrated by numerical examples and Monte Carlo simulations.

Multi-criteria constrained interval type-2 fuzzy decision-making: A space analysis perspective

Practical decision-making problems invariably involve inherent uncertainty in information, making it challenging to obtain precise evaluations. In this research, the utilization of constrained interval type-2 fuzzy sets (CIT2 FSs) enables the representation of complex uncertainty in decision information, and a space analysis approach, called CIT2 fuzzy multi-criteria acceptability analysis (CIT2FMAA), is proposed for conducting reliable decision-making. The CIT2FMAA introduces a novel paradigm for type-2 fuzzy decision-making, wherein CIT2 fuzzy evaluations are acknowledged as spaces of type-1 fuzzy evaluations and processed using type-1 fuzzy operations and methodologies. First, from a space analysis perspective, the type-2 fuzzy measures of logical rank expressions are formalized as CIT2 fuzzy rank acceptability indexes, and the CIT2 fuzzy rank acceptability analysis is developed to rank CIT2 fuzzy numbers. Second, the weighted average of the CIT2 fuzzy numbers is defined, and the CIT2FMAA is proposed to support uncertain multi-criteria decision-making. Subsequently, a sampling algorithm is designed for implementing CIT2 fuzzy rank acceptability analysis and CIT2FMAA. Finally, a linguistic disease diagnosis is performed to demonstrate the efficiency of CIT2FMAA. Compared to traditional type-2 fuzzy decision-making methods, CIT2FMAA is more efficient because it not only derives ranks of alternatives but also provides confidence degrees for those ranks.

Double stochastic preference analysis on group decision making with interval additive preference relations

Interval additive preference relations (IAPRs) provide a valuable framework for representing uncertain pairwise comparisons among alternatives in group decision-making (GDM). Conventional GDM methods that incorporate IAPRs often treat preference values as uniform entities and establish dedicated conflict or consensus mechanisms for amalgamating individual opinions while overlooking the stochastic preferences and lacking the flexibility to address diverse decision requirements. Therefore, to account for the stochastic characteristics in both preference representation and group preference aggregation, this paper introduces an innovative double stochastic preference analysis for GDM with IAPRs. In the initial phase, stochastic additive preference relations are extracted from IAPRs using a specific density function. A best preference matrix is then constructed through a logarithmic consistency minimum deviation model. In the second phase, preference comparison intervals are synthesized to encapsulate the collective viewpoints of decision-makers. Subsequently, ranking acceptability indices and preference confidence degrees are computed through processing of the preference ranking judgment space, leading to the derivation of the final preference rankings. The experimental results, obtained through Monte Carlo simulations for a site selection case in Anhui Province, China, demonstrate the effectiveness and validity of the proposed method. The ranking results achieved a confidence degree exceeding 89%, offering insights into decision-makers’ weight distribution for various ranking scenarios.

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.

Long-term assessment of ecological risk dynamics in Wuhan, China: Multi-perspective spatiotemporal variation analysis

Ecological Risk Assessment (ERA) is a critical component of ecosystem management aimed at identifying potential harms caused by human activities. However, previous studies have been limited by short periods, narrow perspectives, and a focus on single-risk factors, making it difficult to comprehensively assess ecological risk. To address these issues, our study investigates ecological risk changes in Wuhan from 1996 to 2020, adopting a multi-perspective method. We then employed a unified scalar overlay approach that fuses the Landscape Ecological Risk Index (LERI) and Habitat Degradation Index (HDI) to conduct an in-depth analysis of comprehensive ecological risk. It considers the interrelationships between threat sources and receptors, as well as the morphological structure of patches, to extract spatial information of ecological risks. Our results reveal that: (1) Wuhan's cropland decreased by 1760.98km2 (−38.95%), mainly converting to construction land and woodland. Simultaneously, construction land expanded outwards from the city center (+895.72km2). At the landscape level, Wuhan's landscape patches are becoming progressively more fragmented. (2) Total Ecological Risk Index (SERI) retains the “Low-High-Low” pattern of both LERI and HDI from the inside out, with high ecological risks radiating from the central urban boundary towards the surrounding regions. The lower-risk areas of the SERI converted mainly to the low-risk areas, whereas the area of higher-risk experienced a larger increase (+1083.45km2), mainly from high-risk areas. Overall, the SERI of Wuhan tends to move towards the north and east. (3) Spatially, hot-spots of risk are predominantly concentrated in the north of Wuhan, while cold-spots are primarily located in the city center. Confidence degrees of both hot and cold spots decrease from their center to the external areas. Additionally, ecological risk showed significant spatial dependency in its distribution. Given the current global environmental crisis, ecological risk research is more important than ever, and our new ERA framework is developed to provide valuable insights for urban planners to mitigate ecological risk.

Confidence Levels-Based Cubic Fermatean Fuzzy Aggregation Operators and Their Application to MCDM Problems

Assessment specialists (experts) are sometimes expected to provide two types of information: knowledge of rating domains and the performance of rating objects (called confidence levels). Unfortunately, the results of previous information aggregation studies cannot be properly used to combine the two categories of data covered above. Additionally, a significant range of symmetric/asymmetric events and structures are frequently included in the implementation process or practical use of fuzzy systems. The primary goal of the current study was to use cubic Fermatean fuzzy set features to address such situations. To deal with the ambiguous information of the aggregated arguments, we defined information aggregation operators with confidence degrees. Two of the aggregation operators we initially proposed were the confidence cubic Fermatean fuzzy weighted averaging (CCFFWA) operator and the confidence cubic Fermatean fuzzy weighted geometric (CCFFWG) operator. They were used as a framework to create an MCDM process, which was supported by an example to show how effective and applicable it is. The comparison of computed results was carried out with the help of existing approaches.

C_CART: An instance confidence-based decision tree algorithm for classification

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.

Reservoir facies design and modeling using probabilistic rock-physics templates

We have developed a technique to design and optimize reservoir lithofluid facies based on probabilistic rock-physics templates. Subjectivity is promoted to design possible facies scenarios with different pore-fluid conditions, and quantitative simulations and evaluations are conducted in facies model selection. This method aims to provide guidelines for reservoir-facies modeling in an exploration setting in which limited data exist. The work includes two parts: facies-model simulations and uncertainty evaluations. We have first derived scenarios with all possible fluid types using Gassmann fluid substitution. We designed models with different numbers of facies and pore-fluid conditions using site-specific rock-physics templates. Detailed facies simulations were conducted in the petroelastic, elastic, and seismic domains in a step-by-step framework to preserve the geologic interpretability. The use of probabilistic rock-physics templates allowed for multiple realizations of each facies model to account for different types and magnitudes of errors and to infer facies probability and uncertainty. For each realization, we used Bayesian classification to assign facies labels. Comparisons between the predicted and true labels provided the success rates and entropy indices to quantify the prediction errors and confidence degrees, respectively. This workflow was tested with well-log data from a clastic reservoir in the Gulf of Mexico. We simulated models with five to seven facies with different pore-fluid parameters. From the petroelastic, elastic, and seismic domains, the uncertainty of facies models significantly increased due to well-log measurement errors, data-model mismatch, and resolution differences. The facies model consisting of oil sand, gas sand, and shale was the optimal set based on the high success rates and low entropy indices. Facies profiles estimated from this optimal model presented significant consistency with well-log interpretations. The techniques and results demonstrated here could be applied to different types of clastic reservoirs, and they provide useful constraints for reservoir facies modeling during early oilfield exploration stages.

Learning a Twofold Siamese Network for RGB-T Object Tracking

Visual object tracking works as a key component for many instrumentation and measurement applications such as UAV systems, optical tracking and measuring systems. This paper investigates how to implement accurate RGB-T tracking by integrating the complementary information from RGB and thermal sources. Inspired by the success of Siamese networks in the RGB tracking field, we design a twofold Siamese network for RGB-T tracking, which is composed of an RGB branch and a thermal branch. Each branch is a Siamese network, which can be utilized to compute similarities between the search image and the exemplar image. The parameters in the RGB branch are kept the same as SiamFC. The thermal branch is initialized with parameter weights of the trained network in SiamFC and fine-tuned with constructed thermal image pairs to better capture the target characteristics in the thermal data. Two criteria are further proposed to measure the confidence degrees of response maps obtained by these two modalities. The final response map is computed by adaptively fusing them according to their confidence degrees. The maximum location on the final response map is identified as the target location, and the target scale is obtained through a simple multi-scale search. Experiments on the recently public benchmark RGB-T234 demonstrate the effectiveness of our proposed method when compared to other state-of-the-art trackers. The high performance of our proposed trakcer makes it easy to be implemented in embedded system.

Pengaruh Konseling Saat Antenatal Care (ANC) Terhadap Pengetahuan Dan Sikap Ibu Hamil Tentang Tanda-Tanda Bahaya Kehamilan Di Wilayah Kerja Puskesmas Kapasa

Objective: The purpose of this study was to determine the effect of counseling during Antenatal care (ANC) on the knowledge and attitudes of pregnant women about the danger signs of pregnancy in the Kapasa health center coverage area. Methods: In this study, using a pre-experimental design design with the One Group Pre test and Post test approach and using purposive sampling research techniques. The number of samples is 16 people. The sampling technique uses the Federer formula. Data was collected by means of a questionnaire, data analysis used univariate and bivariate analysis using distribution tabels and Wilcoxon statistical test with 95% confidence degrees. Results: The results showed that the results of the Wilcoxon test for knowledge obtained p value = 0.011 which means that there is an effect of counseling on knowledge of pregnant women. And the Wilcoxon test results obtained p value = 0.025, which means there is an effect of counseling on the attitude of pregnant women. Conclusion: there is an effect of counseling during antenatal care on attitudes of pregnant women.

Optimization configuration of energy storage capacity based on the microgrid reliable output power

Abstract Reasonable energy storage capacity in a high source-to-charge ratio local power grid can not only reduce system costs but also improve local power supply reliability. This paper introduces the capacity sizing of energy storage system based on reliable output power. The proposed model is formulated to determine the relationship between the power capacity and wind energy loss, considering the wind curtailment loss and traditional energy power uncertain reserve. The non-parametric kernel density estimation is adopted to estimate the confidence intervals of wind power prediction error and fluctuation ranges of the actual output of a wind farm under different confidence degrees. The actual historical data of scenery resources in a certain area is used to verify the feasibility of the proposed method. The simulation shows the large-capacity energy storage, the reliable output power of the microgrid wind ensures the feasibility of day-ahead generation plans.

An Extended VIKOR Method for Multiple Attribute Decision Making with Linguistic D Numbers Based on Fuzzy Entropy

The linguistic [Formula: see text] numbers (LDNs) can express the fuzzy evaluation information more easily and precisely by combining the advantages of linguistic terms (LTs) and [Formula: see text] numbers (DNs). Existing researches on the fuzzy entropy of LDNs are rare, and most of the definitions of fuzzy entropy for LDNs are unreasonable. In view of this research gap, this paper improves the definition of fuzzy entropy of LDNs, which simultaneously considers the effects of confidence degrees and LTs on the value of fuzzy entropy in LDNs. Then, the weights of attributes can be calculated by the improved fuzzy entropy. Further, a new combination rule for LDNs is also presented in this paper. Based on these studies, we extend the traditional Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to the LDNs and develop the LD-VIKOR method. The proposed LD-VIKOR method is convenient to handle MADM problems with unknown attributes weights under the environment of LDNs. Finally, we verify the validity and reliability of the proposed method by an illustrative example, and analyze the advantages of the proposed method by comparing it with other existing MADM methods.

General Diagnostic Framework Based on Non‐axiomatic Logic for Aviation Safety Event Analysis

To achieve causality reasoning of aviation safety events based on big data of cross-media network, a data-driven general diagnostic framework based on nonaxiomatic logic is designed and implemented. On the basis of this framework, the uncertain causality between aviation safety events and faults is expressed in the form of binary non-axiomatic incident experience at first. A general expression for calculating the attribution and confidence degrees in the non-axiomatic incident experience is given based on records of aviation safety historical incident. A concept of non-axiomatic incident experience graph is proposed, a diagnosis algorithm for aviation safety events is given with the combination of revision and deduction rules in non-axiomatic logic. Experimental results of a Version 1.0 beta demo show that this framework can effectively diagnose all potential faults according to aviation safety events; compared with other machine learning frameworks, it has higher reliability (especially scalability) under the premise of ensuring diagnosis accuracy.

Additive Consistency of Hesitant Fuzzy Linguistic Preference Relation With a New Expansion Principle for Hesitant Fuzzy Linguistic Term Sets

Hesitant fuzzy linguistic preference relation (HFLPR) is a new preference structure that the decision makers (DMs) are hesitant about several possible linguistic terms of preference information for pairwise comparison between alternatives. This paper examines the additive consistency of HFLPR with a new expansion principle for hesitant fuzzy linguistic term sets (HFLTSs). In order to normalize HFLTSs with different numbers of linguistic terms, a least common multiple expansion (LCME) principle is proposed. According to the LCME principle, the additive consistent index of an HFLPR is defined to measure the consistency level of the HFLPR. For improving the unacceptable additive consistency of an HFLPR, a pure integer programming model is constructed to derive an acceptable additive consistent HFLPR. In group decision making (GDM) with HFLPRs, the similarities between DMs are calculated based on their individual HFLPR with acceptable additive consistency. Subsequently, the confidence degrees of DMs are defined to derive DMs’ weights, and some examples including an investment project management problem are analyzed to verify the effectiveness of the proposed method.

The impact of executive anticipated regret on the choice of incentive system: An econophysics perspective

In this paper, we investigate the impact of anticipated regret on the decision of incentive systems and discuss the three groups respectively according to the confidence degrees on the basis of non-parametric test and empirical method. Qualitatively, we find that the choice of incentive systems is significantly different among the three groups and anticipated regret has significant impact on the decision of incentive systems. Quantitatively, we find that the higher “confidence degree” of the executives means greater tendency to choose incentive system related to firm performance, and we also use model to investigate the relationship between anticipated regret and shares of incentive system. We find that the two directions’ anticipated regret has the opposite effect on the decision. From the results of both experimental method and empirical method the hypotheses are verified.

Handling veracity in multi-criteria decision-making: A multi-dimensional approach

Decision support systems aim to help a decision maker with selecting the option from a set of available options that best meets her or his needs. In multi-criteria based decision support approaches, a suitability degree is computed for each option, reflecting how suitable that option is considering the preferences of the decision maker. Nowadays, it becomes more and more common that data of different quality, originating from different data sets and different data providers have to be integrated and processed in order to compute the suitability degrees. Also, data sets can be very large such that their data become commonly prone to incompleteness, imprecision and uncertainty. Hence, not all data used for decision making can be trusted to the same extent and consequently, neither the results of computations with such data can be trusted to the same extent. For this reason, data quality assessment becomes an important aspect of a decision making process. To correctly inform the users, it is essential to communicate not only the computed suitability degrees of the available options, but also the confidence about these suitability degrees as can be derived from data quality assessment. In this paper a novel multi-dimensional approach for data quality assessment in multi-criteria decision making, supporting the computation of associated confidence degrees, is presented. Providing confidence information adds an extra dimension to the decision making process and leads to more soundly decisions. The added value of the approach is illustrated with aspects of a geographic decision making process.

A group decision making method with interval valued fuzzy preference relations based on the geometric consistency

This paper investigates a group decision making (GDM) method with interval valued fuzzy preference relations (IVFPRs). According to the geometric consistency of IVFPR, the max-consistency index and min-consistency index of an IVFPR are developed respectively. Combining the max-consistency index with min-consistency index, the geometric consistent index of an IVFPR is defined to measure the consistency level of the IVFPR by considering decision maker's (DM's) risk attitude. For improving the unacceptable geometric consistency of an IVFPR, a goal programming model is constructed to derive an acceptable geometric consistent IVFPR. By regarding the geometric consistent conditions of an IVFPR as fuzzy constraints, a fuzzy logarithmic program is established to generate the interval priority weights. In GDM problems, the individual interval priority weights are obtained by solving the corresponding fuzzy logarithmic programs. The similarities between DMs are calculated based on their individual interval priority weights. Subsequently the confidence degrees of DMs are defined to determine DMs' weights. To obtain the collective interval priority weights, a parametric linear program is constructed and transformed into a linear program to resolve. The order of alternatives is generated by the collective interval priority weights. Some examples are analyzed to verify the effectiveness of the proposed method.

Модифицированный метод оценки Story Points в методологии разработки Scrum, основанный на теории нечеткой логики

Several known methods allow to estimate the overall effort(s) to be used up for the software development. The approach based on story points is preferable and quite common in the context of Sсrum agile development methodology. However, it might be rather challenging for people, who are new to this methodology or to a specific Scrum team to estimate the amount of work with story points. The proposed approach involves estimation of features on the basis of linguistic terms that are both habitual and clear for everyone. The presented fuzzy inference system (Mamdani’s model) makes it possible to calculate story points using people’s opinions expressed as sentences in natural language - the study shows empirically that beginners to Scrum methodology consider the proposed approach to be more convenient and easier in use than the ‘plain’ story points estimation. Also, four groups of people with different levels of qualification in Scrum were asked to estimate several features of a certain project using the developed approach and common story points approach to prove the relevance of the approach - it was shown that the results of basic story points estimation for Scrum experts differ slightly from the results revealed by proposed approach, while for Scrum beginners such difference is significant. To the opinion of authors, the proposed approach may allow to adapt to Scrum more smoothly, with better understanding of what is implied by story points, grasping the general idea and learning faster their use in practice. The experimental study conducted as a part of the research has shown results approaching the estimations provided by Scrum experts who have been working in real projects and making use of story points for several years. Continuation of the present work can be associated with intensive studies of more complicated methods of aggregation of the experts’ opinions, analysis of alternative representation forms of confidence degrees in estimates provided as well as the development of plugin for JIRA tracking system.

NMC: nearest matrix classification – A new combination model for pruning One-vs-One ensembles by transforming the aggregation problem

The One-vs-One strategy is among the most used techniques to deal with multi-class problems in Machine Learning. This way, any binary classifier can be used to address the original problem, since one classifier is learned for each possible pair of classes. As in every ensemble method, classifier combination becomes a vital step in the classification process. Even though many combination models have been developed in the literature, none of them have dealt with the possibility of reducing the number of generated classifiers after the training phase, i.e., ensemble pruning, since every classifier is supposed to be necessary.On this account, our objective in this paper is two-fold: (1) We propose a transformation of the aggregation step, which lead us to a new combination strategy where instances are classified on the basis of the similarities among score-matrices. (2) This fact allows us to introduce the possibility of reducing the number of binary classifiers without affecting the final accuracy. We will show that around 50% of classifiers can be removed (depending on the base learner and the specific problem) and that the confidence degrees obtained by these base classifiers have a strong influence on the improvement in the final accuracy.A thorough experimental study is carried out in order to show the behavior of the proposed approach in comparison with the state-of-the-art combination models in the One-vs-One strategy. Different classifiers from various Machine Learning paradigms are considered as base classifiers and the results obtained are contrasted with the proper statistical analysis.

Multi‐band joint local sparse tracking via wavelet transforms

A novel multi‐band joint local sparse tracking algorithm via wavelet transforms is proposed in this study. The object image may contain rich information of different types; the authors use wavelet transforms to decompose the object image into some sub‐band images first. This will help extract the information in different frequency ranges for the object. Then same block operation is executed on all the sub‐band images. The l2, 1 mixed‐norm is used to describe the multi‐band joint local sparse representation on each patch; it can effectively extract the structural information in different frequency ranges. Thus, more accurate object appearance model can be established. Second, the coefficients on the diagonal of coefficient matrix are extracted for the confidence degrees of the candidate objects in this band, and then the confidence degree results in all the bands are fused to determine the best candidate object in the current frame. This can effectively alleviate the object drifting. Finally, both qualitative and quantitative evaluation results on 15 challenging video sequences demonstrate that the proposed tracking algorithm in this study can achieve better tracking effects compared with the other state‐of‐the‐art algorithms.