A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

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

Featured Anomaly Detection Methods and Applications

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great demand of introducing more intelligent anomaly detection methods with distinct characteristics to cope with various needs. To this end, this thesis starts with presenting a thorough review of existing anomaly detection strategies and methods. The advantageous and disadvantageous of the strategies and methods are elaborated. Afterward, four distinctive anomaly detection methods, especially for time series, are proposed in this work aiming at resolving specific needs of anomaly detection under different scenarios, e.g., enhanced accuracy, interpretable results, and self-evolving models. Experiments are presented and analysed to offer a better understanding of the performance of the methods and their distinct features. To be more specific, the abstracts of the key contents in this thesis are listed as follows: 1) Support Vector Data Description (SVDD) is investigated as a primary method to fulfill accurate anomaly detection. The applicability of SVDD over noisy time series datasets is carefully examined and it is demonstrated that relaxing the decision boundary of SVDD always results in better accuracy in network time series anomaly detection. Theoretical analysis of the parameter utilised in the model is also presented to ensure the validity of the relaxation of the decision boundary. 2) To support a clear explanation of the detected time series anomalies, i.e., anomaly interpretation, the periodic pattern of time series data is considered as the contextual information to be integrated into SVDD for anomaly detection. The formulation of SVDD with contextual information maintains multiple discriminants which help in distinguishing the root causes of the anomalies. 3) In an attempt to further analyse a dataset for anomaly detection and interpretation, Convex Hull Data Description (CHDD) is developed for realising one-class classification together with data clustering. CHDD approximates the convex hull of a given dataset with the extreme points which constitute a dictionary of data representatives. According to the dictionary, CHDD is capable of representing and clustering all the normal data instances so that anomaly detection is realised with certain interpretation. 4) Besides better anomaly detection accuracy and interpretability, better solutions for anomaly detection over streaming data with evolving patterns are also researched. Under the framework of Reinforcement Learning (RL), a time series anomaly detector that is consistently trained to cope with the evolving patterns is designed. Due to the fact that the anomaly detector is trained with labeled time series, it avoids the cumbersome work of threshold setting and the uncertain definitions of anomalies in time series anomaly detection tasks

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv