Dataset Information

Cellular frustration algorithms for anomaly detection applications.

ABSTRACT: Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emergent dynamics is sensitive to changes in the displayed information and can be used to detect anomalies, which can be important to accomplish the immune system main function of protecting the host. Therefore, it has been hypothesized that these models could be adequate to model the immune system activation. Likewise it has been hypothesized that these models could provide inspiration to develop new artificial intelligence algorithms for data mining applications. However, computational algorithms do not need to follow strictly the immunological reality. Here, we investigate efficient implementation strategies of these immune inspired ideas for anomaly detection applications and use real data to compare the performance of cellular frustration algorithms with standard implementations of one-class support vector machines and deep autoencoders. Our results demonstrate that more efficient implementations of cellular frustration algorithms are possible and also that cellular frustration algorithms can be advantageous for semi-supervised anomaly detection applications given their robustness and accuracy.

SUBMITTER: Faria B

PROVIDER: S-EPMC6613704 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cellular frustration algorithms for anomaly detection applications.

Faria Bruno B Vistulo de Abreu Fernao F

PloS one 20190708 7

Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emergent dynamics is sensitive to changes in the displayed information and can be used to detect anomalies, which can be important to accomplish the immune system main function of protecting the host. Therefore, it has been hypothesized that these models ...[more]

PMID: 31283758

Similar Datasets

Project description:Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Dataset Information

Cellular frustration algorithms for anomaly detection applications.

Publications

Cellular frustration algorithms for anomaly detection applications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets