Can I read Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems on EtoBox?

Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems by Kimia Keshanian; Daniel Zantedeschi; Kaushik Dutta is a Engineering article available to read on EtoBox.

What is Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems about?

Feature selection is a fundamental problem in online advertising, as features usually need to be purchased from third parties, and they are costly. Although many feature selection techniques can be used in online advertising and the general information systems (IS) domain, their performance is often context specific. Therefore, the literature of IS is suffering from a lack of adequate and generic methods. In this study, we address this issue by proposing a novel approach that employs ideas from the field of cooperative game theory. We derive a (continuous) second-order cone program that any convex programming solver can solve for determining the best subset of features. We show the efficacy of our proposed method on a real-life online advertising case study. We demonstrate that our proposed approach performs better in accuracy, precision, recall, and F-1 score than the best of the other approaches with much fewer features. Also, to illustrate that our method’s benefits are not limited to the context of online advertising, we perform an extensive set of simulations and consider a well-established real-life data set drawn from the UCI Machine Learning Repository at the University of

Who reads Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems?

Author

Kimia Keshanian; Daniel Zantedeschi; Kaushik Dutta

Published

2022

Language

EN

Field

Engineering (Physical Sciences)