ANFIS Construction With Sparse Data via Group Rule Interpolation

Abstract

A major assumption for constructing an effective ANFIS (Adaptive-Network-based Fuzzy Inference System) is that sufficient training data is available. However, in many real world applications, this assumption may not hold, thereby requiring alternative approaches. In light of this observation, this research focusses on automated construction of ANFISs in an effort to enhance the potential of Takagi-Sugeno fuzzy regression models, for situations where only limited training data is available. In particular, the proposed approach works by interpolating a group of fuzzy rules in a certain given domain with the assistance of existing ANFISs in its neighbouring domains. The construction process involves a number of computational mechanisms, including: a rule dictionary which is created by extracting rules from the existing ANFISs; a group of rules which are interpolated by exploiting the local linear embedding algorithm to build an intermediate ANFIS; and a fine-tuning method which refines the resulting intermediate ANFIS. Experimental evaluation on both synthetic and real world datasets is reported, demonstrating that when facing the data shortage situations, the proposed approach helps significantly improve the performance of the original ANFIS modelling mechanism.

Image Super Resolution with Sparse Data Using ANFIS Interpolation

“…So the improved performance is related to the interpolation technique. Note also that the computation complexity of the ANFIS interpolation has been analyzed in paper [9]. The contribution of the proposed method is that it can deal with SR problems with sparse training data, while most existing methods only consider the situation with sufficient training data.…”

“…ANFIS interpolation [9] is an extension of classical Fuzzy Rule Interpolation (FRI) methodology [10], [11], aiming to construct an effective target ANFIS A t under the situations of data shortage, by interpolating two neighbouring source ANFISs A s1 and A s2 . The general ANFIS interpolation process can be summarized in the following 3 steps.…”

Approximate reasoning with fuzzy rule interpolation: background and recent advances

“…It provides promising solutions to cyber-security problems, including: network security analysis, intelligent intrusion detection (Naik et al 2017b) and firewall reinforcement (especially for Microsoft Windows Firewall) (Naik et al 2017a). FRI also finds impressive results in performing practical pattern recognition tasks, examples include: classic classification and prediction problems (Li et al 2018b(Li et al , 2020a) using weighted FRI techniques; computer vision and image super resolution (Yang et al 2019); and disease diagnosis in general and mammographic mass risk analysis (Li et al 2019) and colorectal polyp detection (Nagy et al 2018) in particular. Further applications of FRI are found in function approximation Berecz 2009) and student academic performance evaluation (Johanyák 2010).…”

“…This section first formulates the basic idea of the most famous -cut based FRI, named KH linear FRI (after its inventors Kóczy and Hirota 1993a, b), in a general formation, followed by its practical implementation by the use of triangular membership functions in a multidimensional situation. Kovács and Kóczy (1997a, b, c) Interpolation based on approximation of vague environment of fuzzy rules with application to automatic guided vehicle systems , 2001, 2000 Interpolative method based on graduality Jenei (2001), Jenei et al (2002) Axiomatic approach for interpolation and extrapolation of fuzzy quantities Yam et al (2000b), Yam and Kóczy (2000, 2001 and Yam et al (2000a) Cartesian based interpolation with each fuzzy set mapped onto a point in high dimensional Cartesian space Yang et al (2019Yang et al ( , 2021 Group rule interpolation for constructing Adaptive Neuro-Fuzzy Inference System (ANFIS) and an evolutionary computation-supported approach…”

Sparse data-based image super-resolution with ANFIS interpolation

“…Based on this observation, ANFIS interpolation techniques have been developed to provide a potentially credible solution to learning with limited data overall. This type of approach works by training one ANFIS with sparse data through interpolating two adjacent ANFIS models that have been trained with sufficient data, reading to a desirable, and interpretable, non-linear mapping for the problem area where no sufficient training data are available [12]. Note that the interpolation here is carried out at the fuzzy rule level, not at the raw data level.…”