Book Chapter10.1007/978-3-030-73050-5_13
Optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection
Davide Carneiro,Miguel Guimarães,Miguel Sousa +2 more
- 14 Dec 2020
- pp 124-133
2
TL;DR: In this article, the authors present a system for supporting auditors in the task of financial fraud detection, where auditors can provide feedback regarding the instances of the data they use, or even suggest new variables.
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Abstract: Machine Learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation.
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Citations
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References
ilastik: interactive machine learning for (bio)image analysis.
Stuart Berg,Dominik Kutra,Thorben Kroeger,Christoph N. Straehle,Bernhard X. Kausler,Carsten Haubold,Martin Schiegg,Janez Ales,Thorsten Beier,Markus Rudy,Kemal Eren,Jaime I Cervantes,Buote Xu,Fynn Beuttenmueller,Adrian Wolny,Chong Zhang,Ullrich Koethe,Fred A. Hamprecht,Anna Kreshuk +18 more
TL;DR: Ilastik as mentioned in this paper is an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise, which contains pre-defined workflows for image segmentation, object classification, counting and tracking.
Learning under Concept Drift: A Review
TL;DR: A high quality, instructive review of current research developments and trends in the concept drift field is conducted, and a framework of learning under concept drift is established including three main components: concept drift detection, concept drift understanding, and concept drift adaptation.
995
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
TL;DR: Interactive machine learning (iML) is defined as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.”
Learning under Concept Drift: A Review
TL;DR: In this paper, the authors present a review of the recent research in the field of concept drift and propose a framework of learning under concept drift. But, the focus of this survey is on the detection, understanding and adaptation of the concept drift in streaming data.
752
Interactive machine learning: letting users build classifiers
TL;DR: It is shown that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms, and that small expert-defined models offer the additional advantage that they will generally be more intelligible than those generated by automatic techniques.