The 11th International Conference on Fuzzy Systems and Data Mining (FSDM 2025)

Abstract: To be updated

Abstract: Fuzzy C-Means (FCM) is a clustering algorithm based on partition of the universe. However, the partition generated by an equivalence relation is strict in practical application and exhibits relatively poor fault-tolerant mechanism. In this paper, a novel binary relation based on improved FCM with the principle of refined justifiable granularity is presented. Different expressions of the proposed binary relation under different values of weight parameter are discussed, and the changes of the properties of the binary relation under different parameter values are provided. By measuring the significance of attributes in the feature space, a feature selection method, called forward heuristic feature selection (FHFS), is designed to construct the low-dimension feature space based on maximizing the original data and information retention through the defined degrees of aggregation and dispersion. It is shown how the results of feature selection and classification performance vary when the values of the weight factor locate in different ranges. To illustrate the superiority and effectiveness of the proposed FHFS algorithm, nine high-dimensional datasets and eight image datasets from UCI repository are used and compared with other feature selection methods, respectively. The results of experimental evaluation and the significance test show that the proposed learning mechanism is a superior algorithm.

Abstract: Wavelets, renowned for their ability to perform multi-resolution analysis and capture localized time-frequency representations, have long been instrumental in fields like signal processing and image analysis. However, their integration into machine learning opens up exciting new possibilities for addressing critical challenges such as noise reduction, feature extraction, dimensionality reduction, and multi-scale analysis. This talk explores the untapped potential of wavelets within the context of machine learning, highlighting their applications across diverse tasks, including classification, regression, and generative modeling. By examining how wavelets can enhance neural network architectures, improve data preprocessing, and support efficient feature learning, this discussion aims to inspire innovative approaches at the intersection of wavelet theory and machine learning. Emphasis will also be placed on the challenges of designing wavelet-based frameworks, the trade-offs between interpretability and automation, and the prospects for wavelets in emerging ML paradigms such as transformers, reinforcement learning, and federated learning. This talk serves as a call to action for researchers and practitioners to harness the unique strengths of wavelets, fostering interdisciplinary innovation in artificial intelligence and data science.

Abstract: To be updated

Abstract: To be updated