Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems

Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems

A novel Spatio-Temporal Fuzzy Inference System (SPATFIS) and its stability analysis

Modeling an online time series problem is often a challenging task because of the intrinsic dynamical characteristics of the underlying data distribution and the uncertainty stemming from the data. Hence, we propose a novel Spatio-Temporal Fuzzy Inference System (SPATFIS). One of the prime features of SPATFIS lies in the inclusion of memory type neurons which incorporates both spatial and temporal information of the sequences with a dual recurrent structure in its input and defuzzification layers. SPATFIS also proposes a new self-adaptive learning mechanism to add, eliminate and unify its fuzzy rules. This helps it to attain a parsimonious rule base. Furthermore, stability is rigorously inspected and SPATFIS is proved to be stable using Lyapunov's Input to State Stability theorem. The stability analysis encompasses both the structure and the parameter learning phases. To evaluate the efficacy of SPATFIS numerically, it is compared against state-of-the-art self-adaptive neuro-fuzzy systems with benchmark time series problems from the literature. We also evaluate SPATFS' performance under prequential First-Test-Then-Train protocol to show its suitability in handling data stream. The experimental results distinctly indicate SPATFIS to be significantly faster while retaining competitive accuracy and a compact rule base. A thorough statistical analysis is conducted afterwards to further affirm its advantages

Data for: A Novel Spatio-Temporal Fuzzy Inference System (SPATFIS) and its Stability Analysis

Source Code of SPATFI