Publications

Logic-motivated choice of fuzzy logic operators

Many different "and"- and "or"-operations have been proposed for use in fuzzy logic. It is therefore important to select, for each particular application, the operations which are the best for this particular application. Several papers discuss the optimal choice of "and"- and "or"-operations for fuzzy control, when the main criterion is to get the stablest control (or the smoothest or the most robust or the fastest-to-compute). In reasoning applications, however, it is more appropriate to select operations which are the best in reflecting human reasoning, i.e., operations which are "the most logical". In this paper, we explain how we can use logic motivations to select fuzzy logic operations, and show the consequences of this choice. As one of the unexpected consequences, we get a surprising relation with the entropy techniques, well known in probabilistic approach to uncertainty.

Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images

This paper presents a comparative analysis of Deep Learning models and Fuzzy Rule-Based Classifiers (FBRCs) for Brain Tumor Classification from MRI images. The study considers a publicly available dataset with three types of brain tumors and evaluates the models based on their accuracy and complexity. The study involves VGG16, a convolutional network known for its high accuracy, and FBRCs generated via a multi-objective evolutionary learning scheme based on the PAES-RCS algorithm. Results show that VGG16 achieves the highest classification performance but suffers from overfitting and lacks interpretability, making it less suitable for clinical applications. In contrast, FBRCs, offer a good balance between accuracy and explain- ability. Thanks to their straightforward structure, FRBCs provide reliable predictions with comprehensible linguistic rules, essential for medical decision-making.

Hierarchical granular hotspots detection

A proximity approach to DNA based clustering analysis

Entropy and energy measure characterization of resolution of some fuzzy relational equations

International audience

Neural Networks in the Framework of Granular Computing

The study is concerned with the fundamentals of granular computing and its application to neural networks. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (embracing a number of individual entitites) and their ensuing processing. We elaborate on the rationale behind granular computing.

Effective classification using feature selection and fuzzy integration

Corrigendum to “A characterization of electrocardiogram signals through optimal allocation of information granularity” [Artif. Intell. Med. 54 (2012) 125–134]

Multicriteria Decision Making: Scale, Polarity, Symmetry, Interpretability

In this study, we discuss the problem of interpretability of scale versus polarity in multicriteria decision-making problem. Decision making requires aggregation of premises of different characters and types. The influence of premises on a decision to be made may have a different characteristics as well. Some premises may have a positive character, i.e. they vote/agitate towards making a decision, others may fight against a decision. On the other hand, premises can be tempered by priorities, which may affect their character. Therefore, there is a need to discuss different configurations of premises and their priorities. This is the first aspect of our discussion on multicriteria decision making. The second one under discussion is the interpretability of all aspects mentioned so far. In this case, we discuss the representation problems of both premises and priorities. They are usually exhibited as numbers taken from some scale as, for instance, the unipolar unit interval [0,1] or the bipolar unit interval [-1,1]. On the other hand, there is a question raised about a character of premises/priorities, that is, whether they vote pro or contra a decision to be taken and what relations between scales and polarities are.

Book Review: Multicriteria Decision-Making Under Conditions Of Uncertainty: A Fuzzy Set Perspective. John Wiley & Sons. ISBN: 978–1-119–53,492-1.

Abstract This overview is focused on the book reflecting research results on the fundamentals of the theory of multicriteria (multiobjective and multiattribute) decision-making under conditions of uncertainty. The facet of uncertainty is formalized based on a possibilistic (not probabilistic) approach. These results are based on the fuzzy set theory and its fusion with other branches of mathematics of uncertainty. The overview identifies the crucial arguments behind the ultimate need for this theory, reflects the book’s primary objectives, identifies the key possibilities delivered by the presented book's results, and elaborates on real-world problems solved by applying the findings reported in the book. The thorough critical analysis summarizes the advantages and limitations of the main results covered by the book.

Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis

A comparative study of feature extraction methods and their application to P-RBF NNs in face recognition problem

Power efficient hardware implementation of a fuzzy neural network

This paper presents a digital, transistor level implemented neo-fuzzy neural network. This type of neural network is particularly well suited for real-time applications like those encountered in signal processing and nonlinear system identification. We consider in detail a flexible reconfigurable circuit of a single nonlinear synapse of this network. When combining such circuits, single-layer or multilayer networks can be designed. The advantages of the proposed circuit come in the form of reduced redundancy, high data rate due to parallel operation, low power consumption, and an overall flexibility of system configuration.

Analysis of power asymmetry conflict based on fuzzy options graph models

Solution of initial-value problem for linear third-order fuzzy differential equations

Robust Granular Optimization: A Structured Approach for Optimization Under Integrated Uncertainty

Solving optimization problems under hybrid uncertainty bears a heavy computational burden. In this study, we propose a unified structured optimization approach, termed robust granular optimization (RGO), to tackle the optimization problems under hybrid manifold uncertainties in a computationally tractable manner. Essentially, the RGO can be regarded as a complementary fusion of granular computing and robust optimization techniques. The paradigm of RGO consists of three core phases: 1) uncertainty identification, 2) information granulation in which basic granular units (BGUs) are formed, and 3) robust optimization realized over the BGUs. Following the proposed paradigm, we develop two classes of RGO models for general single-stage and two-stage optimization problems with separable and higher order hybrid uncertainties, respectively. It is shown that both types RGO models can be equivalently transformed into linear programs or mixed integer linear programs that can be handled efficiently by off-the-shelf solvers. Furthermore, a target-based tradeoff model is developed to enhance the flexibility of the RGO models in balancing the granularity level (or robustness level) and the solution conservativeness. The tradeoff model can also be efficiently solved by a binary search algorithm. Finally, sufficient computational studies are presented, and comparisons with the existing approaches show that the RGO models can bring much higher computational efficiency and scalability without losing much optimality, and the RGO solutions exhibit a stronger resistance to the uncertainty.

Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification

Fuzzy rough feature selection (FRFS) is an effective means of addressing the curse of dimensionality in high-dimensional data. By removing redundant and irrelevant features, FRFS helps mitigate classifier overfitting, enhance generalization performance, and lessen computational overhead. However, most existing FRFS algorithms primarily focus on reducing uncertainty in pattern classification, neglecting that lower uncertainty does not necessarily result in improved classification performance, despite it commonly being regarded as a key indicator of feature selection effectiveness in the FRFS literature. To bridge uncertainty characterization and pattern classification, we propose a Margin-aware Fuzzy Rough Feature Selection (MAFRFS) framework that considers both the compactness and separation of label classes. MAFRFS effectively reduces uncertainty in pattern classification tasks, while guiding the feature selection towards more separable and discriminative label class structures. Extensive experiments on 15 public datasets demonstrate that MAFRFS is highly scalable and more effective than FRFS. The algorithms developed using MAFRFS outperform six state-of-the-art feature selection algorithms.

Granular computing-based time series anomaly pattern detection with semantic interpretation