Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?

Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Multi-Label Takagi-Sugeno-Kang Fuzzy System

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.Comment: This work has been accepted by IEEE Transactions on Fuzzy System

Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science

Підсистема прийняття рішень на базі нечітких нейронних мереж

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії НАУ".Керівник дипломної роботи: д.т.н., проф., завідувач кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThe purpose of scientific work: development of a subsystem for decision-making on the basis of fuzzy neural networks, improvement of existing algorithms. The thesis considers theoretical and software part of the development of the decision-making subsystem for solving the classification problem. The author substantiates the relevance of using fuzzy neural networks to solve the problem of classification, analyzes the existing topologies of fuzzy neural networks and fuzzy classifiers, basic algorithms to improve results and combine them into a single structure, identified their shortcomings and proposed a solution to eliminate them An optimization and improvement algorithm for solving the classification problem based on the creation of an ensemble of fuzzy neural networks, namely, a fuzzy TSK classifier, is proposed. This software architecture allows you to create a neural classifier that improves the results of an existing solution. And expands the range of calculations performed to classify the input data.Мета наукової роботи: розробка підсистеми для прийняття рішень на базі нечітких нейронних мереж, покращення існуючих алгоритмів. В дипломній роботі розглядається теоретична та програмна частина розробки підсистеми прийняття рішень для розв’язання задачі класифікації. Автором обґрунтовано актуальність використання нечітких нейронних мереж для вирішення задачі класифікації, проведено аналіз існуючих топологій нечітких нейронних мереж та нечітких класифікаторів, основних алгоритмів для покращення результатів та поєднання їх в єдину структуру, виявлено їх недоліки та запропоноване рішення, що дозволяє їх усунути Запропоновано алгоритм оптимізації та покращення для вирішення задачі класифікації на основі створення ансамблю з нечітких нейронних мереж а саме, нечіткого класифікатора TSK. Дана програмна архітектура дозволяє створити нейронний класифікатор який покращує результати уже існуючого рішення. Та розширює спектр виконуваних обчислювань для класифікації вхідних даних