Now showing 1 - 4 of 4
  • Publication
    Handling Noisy Constraints in Semi-supervised Overlapping Community Finding
    Community structure is an essential property that helps us to understand the nature of complex networks. Since algorithms for detecting communities are unsupervised in nature, they can fail to uncover useful groupings, particularly when the underlying communities in a network are highly overlapping [1]. Recent work has sought to address this via semi-supervised learning [2], using a human annotator or “oracle” to provide limited supervision. This knowledge is typically encoded in the form of must-link and cannot-link constraints, which indicate that a pair of nodes should always be or should never be assigned to the same community. In this way, we can uncover communities which are otherwise difficult to identify via unsupervised techniques. However, in real semi-supervised learning applications, human supervision may be unreliable or “noisy”, relying on subjective decision making [3]. Annotators can disagree with one another, they might only have limited knowledge of a domain, or they might simply complete a labeling task incorrectly due to the burden of annotation. Thus, we might reasonably expect that the pairwise constraints used in a real semi-supervised community detection task could be imperfect or conflicting. The aim of this study is to explore the effect of noisy, incorrectly-labeled constraints on the performance of semisupervised community finding algorithms for overlapping networks. Furthermore, we propose an approach to mitigate such cases in real-world network analysis tasks. We treat noisy pairwise constraints as anomalies, and use an autoencoder, a commonlyused method in the domain of anomaly detection, to identify such constraints. Initial experiments on synthetic network demonstrate the usefulness of this approach.
  • Publication
    Radial Basis Function Data Descriptor (RBFDD) Network: An Anomaly Detection Approach
    In this paper, we propose a modification to the standard Ra-dial Basis Function (RBF) network that transforms it into a one-class classifier suitable for anomaly detection. We name this new approach the Radial Basis Function Data Descriptor (RBFDD) network. The RBFDD network is of interest as it has inherent adaptability in its architecture making it suitable for domains in which concept drift is a concern. Also, features learned by an RBFDD network (i.e., centers and spreads of Gaussian kernels and associated weights) provide us with a level of interpretability that has potential to be quite informative in terms of understanding the model learned and the reasoning behind flagging anomalies. In a set of evaluation experiments we compare the performance of the RBFDD network with some state of the art algorithms for anomaly detection over a collection of benchmark anomaly detection datasets. The results show that the RBFDD network is a promising approach and suggest potential for more investigations and promising directions for future work. We also investigate how RBFDD networks can be interpreted.
  • Publication
    Semi-Supervised Anomaly Detection Using One-Class RBF Networks
    (University College Dublin. School of Computer Science, 2021)
    Anomaly detection can be defined as ”the problem of finding patterns in data that do not conform to expected behavior” (Chandola et al., 2009) and has been used in real world applications such as fraud detection (Ghosh and Reilly, 1994), monitoring healthcare (¿Sabi´c et al.), intrusion detection (Ghosh et al., 1998), and crowd motion analysis (Zhou et al., 2016). The main challenge in anomaly detection is the imbalanced nature of the training data, since obtaining data belonging to the anomalous class is usually expensive, if not impossible, while the data belonging to the normal class is much more accessible. Hence, since the training data is almost always dominated by the data instances of the normal class, anomaly detection is sometimes framed as the one-class classification problem. This thesis investigates the possibility of modifying the Radial Basis Function (RBF) networks into effective one-class RBF networks for the task of anomaly detection. RBF networks are selected as the foundation for this thesis due to their attractive properties, which include but are not limited to: being fast and efficient, being interpretable (Jin and Sendhoff, 2003) and being adaptable to dynamic changes (Liu et al., 2020; Han et al., 2011; Rapaka et al., 2003) in the distribution of the data with respect to time. A shallow one-class RBF network is proposed, and then by eliminating certain restrictions, a stronger and more flexible version of these networks is also developed. Finally, a strategy for deepening one-class RBF networks is proposed so that they can work with raw high-dimensional datasets, while benefiting from the automatic feature extraction capability of deep learning. Through evaluation experiments it is shown that these newly proposed approaches lead to anomaly detection models that match or exceed the performance of current state of the art approaches while maintaining the advantages of RBF networks.
  • Publication
    The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection
    This paper introduces the Elliptical Basis Function Data Descriptor (EBFDD) network, a one-class classification approach to anomaly detection based on Radial Basis Function (RBF) neural networks. The EBFDD network uses elliptical basis functions, which allows it to learn sophisticated decision boundaries while retaining the advantages of a shallow network. We have proposed a novel cost function, whose minimisation results in a trained anomaly detector that only requires examples of the normal class at training time. The paper includes a large benchmark experiment that evaluates the performance of EBFDD network and compares it to state of the art one-class classification algorithms including the One-Class Support Vector Machine and the Isolation Forest. The experiments show that, overall, the EBFDD network outperforms the state of the art approaches.
      330Scopus© Citations 1