Zero‐Shot Learning

Kenett, Ron S.

doi:10.1002/9781118445112.stat08446

Wiley StatsRef: Statistics Reference Online 2023

DOI: 10.1002/9781118445112.stat08446

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Zero‐Shot Learning

Ron S. Kenett

Abstract: Zero hot Learning is a machine learning technique that allows a model to classify objects from previously unseen classes, without any specific training for those classes. It generalizes a predictive analytic model to unseen classes without any additional training.

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K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

Sabri,

Verde,

Balzanella

2024

Appl Stoch Models Bus & Ind

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Online transactions have become the dominant and most popular form of online payment in today's digital economy. Due to the growing popularity of e‐commerce and the convenience it offers, both consumers and businesses are rapidly adopting online transactions. Notably, credit cards have become one of the most popular and standard online payment methods. However, it should be noted that credit card transactions are not without challenges. In particular, detecting and preventing fraudulent transactions is a major concern of the online payment system. It is difficult to find an effective detection model that can detect the new patterns created by fraudsters, due to the constant evolution of their methods to exploit the vulnerability of current security protocols. These fraud patterns are evolving and may not correspond to existing documented models, leading to a reduction in their identification. In addition, the customer's behavior can affect the model detection as it is susceptible to change based on factors such as economic conditions, trends, and individual circumstances. When consumers deviate from their typical behavior, the model may generate false alerts, thereby reducing its ability to differentiate between legitimate and fraudulent transactions. This article presents a new supervised detection model, called K‐Fuse, which introduces an unsupervised phase in order to detect fraud patterns that may correspond to innovative models introduced by fraudsters. K‐Fuse is a supervised classification method that fuses three steps consisting of (i) unsupervised clustering to identify hidden patterns of transactions in a dataset, (ii) a novel feature selection criterion based on the unsupervised results, and (iii) supervised classification to exploit the results of clustering and feature selection to predict new transactions as fraudulent or legitimate.

show abstract

K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

Sabri,

Verde,

Balzanella

2024

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

Adversarial AI in insurance: an overview

Cattaneo,

Kenett,

Luciano

2023

Eur. Actuar. J.

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Zero‐Shot Learning

Abstract: Zero hot Learning is a machine learning technique that allows a model to classify objects from previously unseen classes, without any specific training for those classes. It generalizes a predictive analytic model to unseen classes without any additional training.

Cited by 2 publications

References 6 publications

K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

Adversarial AI in insurance: an overview

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