Search Results for author: Shubhomoy Das

Found 6 papers, 5 papers with code

Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

2 code implementations23 Jan 2019 Shubhomoy Das, Md. Rakibul Islam, Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa

In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.

Active Learning Anomaly Detection

GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning

2 code implementations2 Oct 2018 Md. Rakibul Islam, Shubhomoy Das, Janardhan Rao Doppa, Sriraam Natarajan

Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors.

Anomaly Detection

Active Anomaly Detection via Ensembles

2 code implementations17 Sep 2018 Shubhomoy Das, Md. Rakibul Islam, Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa

First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.

Active Learning Anomaly Detection

Incorporating Feedback into Tree-based Anomaly Detection

2 code implementations30 Aug 2017 Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md Amran Siddiqui

Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.

Anomaly Detection

A Meta-Analysis of the Anomaly Detection Problem

1 code implementation3 Mar 2015 Andrew Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, Weng-Keen Wong

The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.

Anomaly Detection Experimental Design +1

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