Towards designing and measuring interpretable hierarchical fuzzy systems

This thesis presents a detailed study of the interpretability of hierarchical fuzzy systems (HFSs). It focuses on the development of a design guidelines framework for interpretable HFSs. This thesis aims to fill some of the gaps in the body of knowledge. Several research questions are raised, including: “How can the interpretability of HFSs be measured with indices?”, “How can complexity be comprehensively measured in HFSs?”, “How can user perception on the interpretability and complexity of HFSs be captured?” and “How can interpretable HFSs be designed?” Thus, to study the interpretability of HFSs, this thesis includes the following methodology discussed in different chapters. First, measures of interpretability are investigated, and an index measuring interpretability specifically in HFSs is introduced. The best way to know about interpretability is by learning how to measure it. Although many researchers have suggested indices to measure interpretability, none of them try to measure the interpretability of HFSs. Indeed, all of them only focus on measuring the interpretability of fuzzy logic systems (FLSs). This is due to the HFSs’ architecture, i.e., multiple subsystems, layers, and topologies, and this presents a significant challenge to measure the interpretability of HFSs. Based on this investigation, this study successfully introduces an initial index for measuring the interpretability of HFSs. The initial index is built based on the challenges arising from the structure of HFSs mentioned. Due to the subjective nature of the interpretability, the best way to validate the proposed measurements of interpretability of HFSs is by asking the users. However, it is not an easy task to get the user perception, particularly on the interpretability, and there is a lack of research on this issue. Therefore, the second focus of this thesis presents research on a new method of capturing user perceptions of the interpretability and also complexity of HFSs. This is the first time that user study has been used to obtain and assess both qualities in HFSs. Based on this, a new analysis of the relationship between interpretability and complexity of HFSs is presented, and this provides insights into the process of developing measures of interpretability of HFSs. However, rather than just using the user study to evaluate the measurements directly, this study also uses input from the user study to ‘guide’ the measurement of interpretability of HFSs in what is known as a participatory design approach. The participatory design approach enables the subjective views of a range of users to be taken into account in shaping the measurement of interpretability of HFSs. Thus, the use of the participatory design approach to configure the resulting measurement of interpretability of HFSs is also evaluated. Complexity is seen as an essential component in determining interpretability. In FLSs, complexity is expressed by the number of rules, variables, and fuzzy terms, called rule-based complexity. Several studies have used indicators (for example, the number of rules) to measure the complexity of FLSs. However, none of the studies considered the structure of HFSs, i.e., multiple subsystems, layers and varied topologies, which may also affect the complexity of HFSs. In addition, the user study revealed different perceptions of complexity in HFSs. Therefore, research is then presented on improving the complexity measurement in HFSs. The measurement is based on combining rule-based complexity with the structural complexity. Designing an interpretable HFS is a challenging task because of the need to define the interpretability of the architecture of the HFS (the subsystems, the input variables of each subsystem, and the interactions between subsystem), as well as the rules of each subsystem. To assist with this, a design guidelines framework for interpretable HFSs is produced. The framework is based on the measurement index of interpretability and complexity that is presented earlier. The framework consists of five guidelines for building interpretable HFSs. Finally, to demonstrate a design guidelines framework for interpretable HFSs on the realworld example, a design for an interpretable HFS for a neonatal intensive care unit (NICU) is produced. It is aimed to provide understandable decision support model to clinicians. In the medical context, it is essential for people to understand the importance of the system features. The HFSs is then practically illustrated by using real physiological data at NICU and compared with a flat FLS system. The results show that the design guidelines framework can offer the ability to design an interpretable HFS in practice efficiently

Novel Fuzzy Systems for Human-Autonomous Agent Teaming

University of Technology Sydney. Faculty of Engineering and Information Technology.The Multi-agent Teaming (MAT) systems that have been widely applied in many fields provide a novel method for establishing models, conducting the analysis, implementing complex tasks and so on. The agents in an MAT system can be defined as intelligent agents, machine agents and human agents based on a particular task to exhibit flexible behaviours. This research investigates various fuzzy models to resolve the problems of designing MAT systems. The main contributions are as follows. 1) For multiple-agent coordination, a hierarchical fuzzy system is proposed and applied to navigation and simultaneous arrival of mobile agents. 2) Explainable fuzzy systems are proposed. We developed an interpretable fuzzy model for human agents to understand the decision rules learned by machine agents and a fuzzy rule information visualisation framework for machine agents to understand human cognitive states. 3) Finally, the distributed fuzzy system is proposed to resolve the data privacy and high-dimensional data in designing MAT systems. A novel consensus learning is developed for the distributed fuzzy system to learn antecedent and consequent components

An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys

In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl

An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing

In this paper, a systematic multi-objective fuzzy modeling approach is proposed, which can be regarded as a three-stage modeling procedure. In the first stage, an evolutionary based clustering algorithm is developed to extract an initial fuzzy rule base from the data. Based on this model, a back-propagation algorithm with momentum terms is used to refine the initial fuzzy model. The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency. To tackle the problem of simultaneously optimizing the structure and parameters, a variable length coding scheme is adopted to improve the efficiency of the search. The proposed modeling approach is applied to a real data set from the steel industry. Results show that the proposed approach is capable of eliciting not only accurate but also transparent fuzzy models

A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets