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Fuzzy classifiers have been studied in the area of fuzzy sets for a long time resulting in a number of architectures. In this study, we thoroughly investigate and critically assess fuzzy rule-based classifiers. A topology of the classifier is discussed along with a discussion of the role of fuzzy set technology in the construction of condition and conclusion parts of the classification rules. Some optimization mechanisms utilized in the adjustment of information granules forming the rules are presented. Performance of the fuzzy classifiers is quantified in terms of their accuracy and an area under curve ( AUC ) determined for the receiver operating characteristics ( ROC ). The performance of the classifier is evaluated vis-à-vis a collection of triangular norms used in the construction of the fuzzy classifiers. Experimental studies involve synthetic and publicly available data. Furthermore, comparative studies include the experiments with the commonly used non-fuzzy classifiers. [ABSTRACT FROM AUTHOR]

It becomes apparent that there are no ideal numeric models. Bringing a concept of information granularity to the original numeric model makes it well aligned with the experimental data and helps deliver a better insight into the credibility of the results provided by the model. Information granularity is regarded as a crucial design asset being optimally allocated across the numeric parameters of the originally constructed model. The underlying objective of this study is to propose a concept of a granular output space and develop an optimization process of allocation of information granularity across this space. The optimization is carried out by optimizing output information granules produced by the granular model by considering a product of the essential criteria describing information granules, namely specificity and coverage. The detailed optimization procedure involving Particle Swarm Optimization (PSO) is presented. We stress a generality of the approach that cuts across a variety of classes of models. A collection of experimental studies involving interval information granules is reported demonstrating the main features of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, granular fuzzy models have become an intensively studied category of fuzzy models. Granular fuzzy models help elevate the existing models to the higher level of abstraction subsequently making the resulting constructs in a better rapport with real-world systems. In contrast to numeric models, granular models produce results in the form of information granules (such as intervals, fuzzy sets, rough sets and alike). A number of studies have been focused on efficient designing granular fuzzy models where the information granularity has been formalized in the form of intervals. In this study, we propose a novel conceptual and algorithmic approach by elevating existing fuzzy models to granular fuzzy models in the form of fuzzy sets. Concentrating on Takagi-Sugeno fuzzy rule-based models (being commonly used in the literature), we establish a way of an optimal allocation of information granularity across the parameters of the original model and making them fuzzy sets (fuzzy numbers). As a result, the outputs of the granular fuzzy model become fuzzy sets. In the process of allocation of information granularity, we resort ourselves to some population-based meta-heuristic optimization techniques such as particle swarm optimization and differential evolution. To evaluate the performance of granular fuzzy models, we engage the principle of justifiable information granularity leading to the assessment and optimization of the coverage and specificity of the resulting fuzzy set. Besides, we involve the defuzzification (decoding) process to evaluate the impact of granular parameters on the quality of the numeric manifestation of the model. Comprehensive experimental studies involving synthetic and publicly available data sets are reported to demonstrate the performance of the granular fuzzy models. [ABSTRACT FROM AUTHOR]

Fuzzy rule-based models, due to modular architecture, have attracted attention and resulted in some practical implications because of their nonlinear characteristics and substantial interpretability. Data-driven fuzzy modeling is one of the most prevailing approaches, and the performance of such fuzzy models has been directly affected by a fundamental bias–variance dilemma. The concept and ensuing topologies of the ensemble strategy (bagging and boosting) offer an efficient method for constructing models to address this dilemma and to achieve a sound tradeoff. In this study, we design an ensemble fuzzy rule-based model in the setting of random forest and boosting mechanisms. To demonstrate the feasibility of the proposed method, we focus on the regression type of models. First, we design a method for assembling fuzzy rule-based models to improve the prediction accuracy. Second, we quantify the performance of the ensemble mechanism. To illustrate the effectiveness and discuss the main features of the proposed method, a series of publicly available datasets are considered in the experimental studies. • We design bagging and boosting mechanisms of assembling fuzzy rule-based models. • We quantify and analyze the performance of the ensemble mechanism. • We thoroughly study the predominant parameters of resulting ensemble models. [ABSTRACT FROM AUTHOR]

Currently, there are only a few cases of both mechanical stimulation-induced and seeding-triggered fluorescence conversion in which the mechanism of phase transition remains less well investigated. Herein, the first report of a luminescent switch based on mechano-/seeding-triggered crystal-crystal phase transition in a cocrystal polymorphic system is present. Based on the structure analysis and theoretical calculations, the luminescent switching can be attributed to the orbital overlap of the chromophores and exciton coupling. Furthermore, a mechanism of the phase transition was proposed, in which the synthons undergo movements including slipping and twisting during the phase transition. The effects of heating, local mechanical stimulus, and seed contact on the transition were discussed. It was proposed that contacting seeds can lower the energy barrier of the phase transition by inducing the formation of intermediate transition states of synthons via molecular recognition. The energy barrier was found to be the key factor in such mechano-/seeding-triggered phase transition. Green-to-orange luminescent switching triggered by mechanical force and seed contacting in a cocrystal polymorphic system is demonstrated. Local seed contacting can arise domino-like phase transition, which may follow a mechanism of synthon transformation and energy barrier decreasing via molecular recognition. [Display omitted] • The first report of mechano-/seed-triggered luminescent switch in cocrystals is present. • Luminescent switch is based on the crystal-crystal transition involving large packing changes. • A mechanism is proposed to interpret the phase transition on both mechanical stimulation and seed contacting. • The work provides a strategy to design new mechanochromic luminescent materials. [ABSTRACT FROM AUTHOR]

The notions of multi-granulation and multi-scale are two important issues for the granular computing, both of which can describe the granular structure in certain ways. In this paper, we investigate the belief structure and the plausibility structure by defining belief and plausibility functions from the multi-granulation viewpoint, and discuss how to construct multi-granulation rough set (MGRS) models in multi-scale information systems (MSISs). Based on the MGRS in MSISs, the optimal scale selection methods with various requirements are studied in two aspects of optimistic and pessimistic multi-granulation for a multi-scale decision information system (MSDIS). To interpret and understand the proposed theories, some important properties of optimistic and pessimistic multi-granulation optimal scale selection are analyzed for the MSDIS, which could indicate the inner relationships among the different selection methods of the optimal scales. Furthermore, an example is provided to illustrate and verify these investigated properties. • Multigranulation-based belief and plausibility functions and structures are proposed. • Multigranulation rough sets in multi-scale information systems are presented. • Multigranulation-based optimal scale selection for multi-scale information systems are discussed. • Important properties of multigranulation-based optimal scale selection are analyzed. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to compare primary efficacy indicators of a low-cost, electrospun, nanoscale P(LLA-CL)/fibrinogen patch with a porcine small intestine submucosa patch for hernia repair.Study Design: A randomized, single-blind, controlled multicenter trial was performed in 3 hospitals in Shanghai. Eligible patients (20 to 75 years old) with primary unilateral, reducible groin hernias were randomly assigned (1:1) to electrospun nanoscale P(LLA-CL)/fibrinogen patch (experimental group) or porcine small intestine submucosa (control group) patch groups. Patients were treated with the Lichtenstein technique, and the primary endpoint was hernia recurrence at 33 months after surgery. The secondary endpoints were postoperative complications including groin pain and operative site infections.Results: Between July 2014 and February 2016, 172 patients were assigned to experimental (n = 86) and control (n = 86) groups. At 6-month follow-up, postoperative complications occurred in 5 patients (5 of 86, 5.81%) and 2 (2 of 86, 2.35%) patients in the control and experimental groups, respectively (p < 0.05). At 33-month follow-up, recurrence was observed in 2 patients (2 of 79, 2.53%) in the control group vs none in the experimental group (0 of 78) (the 95% CI difference between the experimental and control groups was -0.93% to 6.00% and within the preset noninferior margin of Δ10%). No significant differences were found in the degree of chronic pain and complications 33 months after surgery between the 2 groups.Conclusions: Because the recurrence rates and postoperative complications after 33 months were not inferior in the experimental group, we believe that the P(LLA-CL)/fibrinogen patch, as a low cost alternative, has prospects for widespread clinical use. [ABSTRACT FROM AUTHOR]