IOGOD: An interpretable outlier generation-based outlier detector for categorical databases-Reference-Cited by-同舟云学术

IOGOD: An interpretable outlier generation-based outlier detector for categorical databases

Published:2022-06 Issue: Volume:195 Page:116570
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zenkl-Galaz Michael Alexander^ORCID,Loyola-González Octavio^ORCID,Medina-Pérez Miguel Angel^ORCID

Funder

Consejo Nacional de Ciencia y Tecnología

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference99 articles.

1. Resilience and burnout in healthcare students and inpatient psychiatric nurses: A between-groups study of two populations;Abram;Archives of Psychiatric Nursing,2020

2. Outlier analysis;Aggarwal,2015

3. A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer;Alazzam;Expert Systems with Applications,2020

4. The measurement and improvement of maternity service performance through inspection and rating: An observational study of maternity services in acute hospitals in England;Allen;Health Policy,2020

5. Dataset on the relationship between consumer satisfaction, brand attitude, brand preference and purchase intentions of dairy product: The case of the Laayoune-Sakia El Hamra region in Morocco;Boubker;Data in Brief,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Saliency infused dialogue response generation: Improving task oriented text generation using feature attribution;Expert Systems with Applications;2024-12

2. Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction;Diagnostics;2024-06-26

3. Outlier detection in a multiset-valued information system based on rough set theory and granular computing;Information Sciences;2024-02

4. Class-overlap undersampling based on Schur decomposition for Class-imbalance problems;Expert Systems with Applications;2023-07