Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online evolving Spiking Neural Network for Unsupervised Anomaly Detection algorithm (OeSNN-UAD), which, unlike OeSNN, works in an unsupervised way and does not separate output neurons into disjoint decision classes. OeSNN-UAD uses our proposed new two-step anomaly detection method. Also, we derive new theoretical properties of neuronal model and input layer encoding of OeSNN, which enable more effective and efficient detection of anomalies in our OeSNN-UAD approach. The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories. Our approach outperforms the other solutions provided in the literature in the case of data streams from the Numenta Anomaly Benchmark repository. Also, in the case of real data files of the Yahoo Anomaly Benchmark repository, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.P. Maciąg acknowledges financial Support of the Faculty of the Electronics and Information Technology of the Warsaw University of Technology, Poland (Grant No. II/2019/GD/1). J.L. Lobo and J. Del Ser would like to thank the Basque Government, Spain for their support through the ELKARTEK and EMAITEK funding programs. J. Del Ser also acknowledges funding support from the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education of the Basque Governmen

Maintenance of Reducts in the Variable Precision Rough Sets Model

Definitions of a reduct for a single object, decision class and all objects of decision table for the variable precision rough sets model are introduced. The definitions have a property that the set of prime implicants of minimal disjunctive normal form of a discernibility function is equal to the set of reducts. Thus the problem of reducts maintenance in dynamically extended information systems is equivalent to the problem of discernibility function maintenance. We prove that the problem can be stated in the form of a Boolean equation: g h = f k, where f , h and k are given monotonic Boolean functions and g is a function to be determined in minimal disjunctive normal form. An incremental algorithm finding the solution of the above equation is proposed. 1 Introduction The variable precision rough sets model (VPRS) [1] is an extension of the rough sets model (RS) [2]. The model was proposed to analyse and identify data patterns which represent statistical trends rather than functiona..

Foundation of intelligent systems: 19th International Symposium, ISMIS 2011. Warsaw, Poland, June 2011: Proceedings

Heidelbergxix, 746 p.: fig., index; 25 c

6th International Conference on Pattern Recognition and Machine Intelligence

This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results

Intelligent tools for building a scientific information platform: advanced architectures and solutions

This book is a selection of results obtained within two years of research per- formed under SYNAT - a nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The selection refers to the research in artificial intelligence, knowledge discovery and data mining, information retrieval and natural language processing, addressing the problems of implementing intelligent tools for building a scientific information platform.This book is a continuation and extension of the ideas presented in “Intelligent Tools for Building a Scientific Information Platform” published as volume 390 in the same series in 2012. It is based on the SYNAT 2012 Workshop held in Warsaw. The papers included in this volume present an overview and insight into information retrieval, repository systems, text processing, ontology-based systems, text mining, multimedia data processing and advanced software engineering.

Intelligent tools for building a scientific information platform from research to implementation

This book is a selection of results obtained within three years of research performed under SYNAT—a nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The book is intended to be the last of the series related to the SYNAT project. The previous books, titled “Intelligent Tools for Building a Scientific Information Platform” and “Intelligent Tools for Building a Scientific Information Platform: Advanced Architectures and Solutions”, were published as volumes 390 and 467 in Springer's Studies in Computational Intelligence. Its contents is based on the SYNAT 2013 Workshop held in Warsaw. The papers included in this volume present an overview and insight into information retrieval, repository systems, text processing, ontology-based systems, text mining, multimedia data processing and advanced software engineering, addressing the problems of implementing intelligent tools for building a scientific information platform