DROPS

Document

AutoML for Explainable Anomaly Detection (XAD)

Authors: Nikolaos Myrtakis, Ioannis Tsamardinos, and Vassilis Christophides

Published in: OASIcs, Volume 119, The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen (2024)

Abstract

Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a given sample (record) is labelled as an anomaly and thus diagnose its root causes. We propose the following reduced-dimensionality, surrogate model approach to explain detector decisions: approximate the detection model with another one that employs only a small subset of features. Subsequently, samples can be visualized in this low-dimensionality space for human understanding. To this end, we develop PROTEUS, an AutoML pipeline to produce the surrogate model, specifically designed for feature selection on imbalanced datasets. The PROTEUS surrogate model can not only explain the training data, but also the out-of-sample (unseen) data. In other words, PROTEUS produces predictive explanations by approximating the decision surface of an unsupervised detector. PROTEUS is designed to return an accurate estimate of out-of-sample predictive performance to serve as a metric of the quality of the approximation. Computational experiments confirm the efficacy of PROTEUS to produce predictive explanations for different families of detectors and to reliably estimate their predictive performance in unseen data. Unlike several ad-hoc feature importance methods, PROTEUS is robust to high-dimensional data.

Cite as

Nikolaos Myrtakis, Ioannis Tsamardinos, and Vassilis Christophides. AutoML for Explainable Anomaly Detection (XAD). In The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen. Open Access Series in Informatics (OASIcs), Volume 119, pp. 8:1-8:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{myrtakis_et_al:OASIcs.Tannen.8,
  author =	{Myrtakis, Nikolaos and Tsamardinos, Ioannis and Christophides, Vassilis},
  title =	{{AutoML for Explainable Anomaly Detection (XAD)}},
  booktitle =	{The Provenance of Elegance in Computation - Essays Dedicated to Val Tannen},
  pages =	{8:1--8:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-320-1},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{119},
  editor =	{Amarilli, Antoine and Deutsch, Alin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tannen.8},
  URN =		{urn:nbn:de:0030-drops-201049},
  doi =		{10.4230/OASIcs.Tannen.8},
  annote =	{Keywords: Anomaly Explanation, Predictive Explanation, Anomaly Interpretation, Explainable AI}
}

Document

DOI: 10.4230/DagSemProc.04391.9

Integrating XML Data Sources using RDF/S Schemas: The ICS-FORTH Semantic Web Integration Middleware (SWIM)

Authors: Ioanna Koffina, Giorgos Serfiotis, Vassilis Christophides, Val Tannen, and Alin Deutsch

Published in: Dagstuhl Seminar Proceedings, Volume 4391, Semantic Interoperability and Integration (2005)

Abstract

Semantic Web (SW) technology aims to facilitate the integration of legacy data sources spread worldwide. Despite the plethora of SW languages e.g., RDF/S, OWL recently proposed for supporting large scale information interoperation, the vast majority of legacy sources still rely on relational databases RDB published on the Web or corporate intranets as virtual XML. In this paper, we advocate a Datalog framework for mediating high level queries to relational and or XML sources using community ontologies expressed in a SW language such as RDF/S. We describe the architecture and the reasoning services of our SW integration middleware, called SWIM, and we present the main design choices and techniques for supporting powerful mappings between different data models, as well as reformulation and optimization of queries expressed against mediation schemas and views.

Cite as

Ioanna Koffina, Giorgos Serfiotis, Vassilis Christophides, Val Tannen, and Alin Deutsch. Integrating XML Data Sources using RDF/S Schemas: The ICS-FORTH Semantic Web Integration Middleware (SWIM). In Semantic Interoperability and Integration. Dagstuhl Seminar Proceedings, Volume 4391, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

Copy BibTex To Clipboard

@InProceedings{koffina_et_al:DagSemProc.04391.9,
  author =	{Koffina, Ioanna and Serfiotis, Giorgos and Christophides, Vassilis and Tannen, Val and Deutsch, Alin},
  title =	{{Integrating XML Data Sources using RDF/S Schemas: The ICS-FORTH Semantic Web Integration Middleware (SWIM)}},
  booktitle =	{Semantic Interoperability and Integration},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4391},
  editor =	{Y. Kalfoglou and M. Schorlemmer and A. Sheth and S. Staab and M. Uschold},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04391.9},
  URN =		{urn:nbn:de:0030-drops-340},
  doi =		{10.4230/DagSemProc.04391.9},
  annote =	{Keywords: integration , xml , rdf schema}
}

Search Results

Documents authored by Christophides, Vassilis

AutoML for Explainable Anomaly Detection (XAD)

Abstract

Cite as

Integrating XML Data Sources using RDF/S Schemas: The ICS-FORTH Semantic Web Integration Middleware (SWIM)

Abstract

Cite as

Thanks for your feedback!

Could not send message