Session Program


  • 10 July 2017
  • 02:00PM - 04:00PM
  • Room: Auditorium
  • Chairs: José Antonio Sanz and Mikel Galar

Fuzzy Methods and Data Mining II

Abstract - The aim of this paper is to present the results of the competition of time series forecasting using soft computing methods that was organized within the IEEE World Congress on Computational Intelligence (IEEE WCCI) in 2016.
Abstract - We further develop our concept of a compound query (cf. Kacprzyk and Zadrozny, 2013) in which in a bipolar query comprising of a required and desired condition aggregated via a non-conventional operator corresponding to ``and if possible'' the particular required and desired conditions are by themselves queries with fuzzy linguistic quantifiers. We use our approach to the dealing with data quality (trustworthiness), originally developed for the queries with fuzzy linguistic quantifiers (cf. Kacprzyk and Zadrozny, 2015), employ it for the required and desired conditions (queries with fuzzy linguistic quantifiers), and then implant into the compound query, i.e. a bipolar query with the required and desired conditions being queries with fuzzy linguistic quantifiers. A new conceptual quality, functionality and human consistency is therefore obtained.
Abstract - Vitamin B12 deficiency is a common disorder with severe impacts on hematological and neurological disorders. Identifying vitamin B12 deficiency is not straightforward since blood vitamin B12 levels are not representative for actual vitamin B12 status in tissue. Instead, methylmalonic acid (MMA) levels in the plasma are used as indicators of vitamin B12 deficiency. MMA concentrations increase starting from the early course of vitamin B12 deficiency but they may also be high regardless of vitamin B12 deficiency due to renal failure (measured by eGFR). In this paper we propose the use of probabilistic fuzzy systems (PFS) to explore the relationship between MMA plasma levels with vitamin B12 and kidney function. We propose a PFS model for the analysis of overall MMA properties for all patients and also specific MMA properties for individual patients. We show that this PFS model leads to accurate MMA interval predictions. We further show that the proposed model can be used to assess a change in the eGFR level to a normal eGFR level, and its effect on the patient's MMA distribution.
Abstract - The classical fuzzy discriminant analysis with kernel methods (KFDA) is an effective method of solving nonlinearity pattern analysis problem. In some complicated cases, the kernel machine constituted by a single kernel function is not able to meet some practical application requirements, such as hetero- geneous information or unnormalised data, non-flat distribution of samples, etc. By searching for an appropriate linear combination of base kernel functions or matrices, multiple kernel learning (MKL) is able to improve the performance in some extent. So it is a necessary choice to introduce multiple kernel learning into KFDA in order to get better results. In this study, multiple kernel fuzzy discriminant analysis (MKFDA) is proposed. Our method obtains the projection matrix from fuzzy discriminant analysis with multiple kernel, and then feature extraction and classification are made based on the projection matrix. The experiment on the AVIRIS image was performed, and the results showed that the performance of fuzzy discriminant analysis with multiple kernels is better than that of fuzzy discriminant with single kernel for the Hyperspectral images' feature extraction and classification.
Abstract - We present a new kernel on fuzzy sets: the Cross Product kernel on fuzzy sets. This kernel implements a similarity measure between fuzzy sets with a geometrical interpretation in Reproducing Kernel Hilbert Spaces. We prove that this kernel is a convolution kernel that generalizes the widely know kernel on sets towards the space of fuzzy sets. Moreover, we show that the Cross Product kernel on fuzzy sets performs an embedding of probability measures into a Reproduction Kernel Hilbert space. Finally, we validated the kernel performance through experiments on supervised classification on noisy datasets.