QUESTO: Interactive Construction of Objective Functions for Classification Tasks

Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta‐information through the design of th...

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Vydáno v:	Computer graphics forum Ročník 39; číslo 3; s. 153 - 165
Hlavní autoři:	Das, Subhajit, Xu, Shenyu, Gleicher, Michael, Chang, Remco, Endert, Alex
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.06.2020
Témata:	Algorithms CCS Concepts Classification Computing methodologies → Model construction and selection Human‐centered computing → Visual analytics Machine learning Machine learning task → Classification Mathematics of computing → Interactive objective functions Specifications User satisfaction
ISSN:	0167-7055, 1467-8659
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta‐information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain‐specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0167-7055 1467-8659
DOI:	10.1111/cgf.13970