Regression Tree Based Explanation for Anomaly Detection Algorithm

Autor: Iñigo López-Riobóo Botana, Carlos Eiras-Franco, Amparo Alonso-Betanzos

Publikováno v: Proceedings, Vol 54, Iss 1, p 7 (2020)

This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate detections on mixed numerical and catego

Externí odkaz: https://doaj.org/article/2f77274052f844a6b628cc7397d3e175

Scalable feature selection using ReliefF aided by locality‐sensitive hashing

Autor: Carlos Eiras-Franco, Amparo Alonso-Betanzos, Antonio Bahamonde, Bertha Guijarro-Berdiñas

Publikováno v: RUC. Repositorio da Universidade da Coruña
instname
Scopus
RUO. Repositorio Institucional de la Universidad de Oviedo

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb6648394cc2f3fe77baec093b1a67b4
https://doi.org/10.1002/int.22546

Regression Tree Based Explanation for Anomaly Detection Algorithm

Autor: Carlos Eiras-Franco, Iñigo Luis López-Riobóo Botana, Amparo Alonso-Betanzos

Publikováno v: RUC. Repositorio da Universidade da Coruña
Universitat Oberta de Catalunya (UOC)
RUC: Repositorio da Universidade da Coruña
Universidade da Coruña (UDC)
Proceedings, Vol 54, Iss 7, p 7 (2020)

[Abstract] This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate detections on mixed numerical

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2e0c3ad61cc0582d122512b8193df9a
http://hdl.handle.net/2183/26501

A scalable decision-tree-based method to explain interactions in dyadic data

Autor: Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Carlos Eiras-Franco, Antonio Bahamonde

Publikováno v: Scopus
RUO. Repositorio Institucional de la Universidad de Oviedo
instname
RUO: Repositorio Institucional de la Universidad de Oviedo
Universidad de Oviedo (UNIOVI)

Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, the existing methods have poor explainabil

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a737363b1b9bbc026a647129da4ff660
http://hdl.handle.net/10651/53678

Interpretable Market Segmentation on High Dimension Data

Autor: Antonio Bahamonde, Carlos Eiras-Franco, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas

Publikováno v: Proceedings, Vol 2, Iss 18, p 1171 (2018)
RUC. Repositorio da Universidade da Coruña
instname

Trátase dun resumo estendido da ponencia [Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89c8f108557800f02cf997d322961100
https://doi.org/10.3390/proceedings2181171