NDLI: Probabilistic Reframing for Cost-Sensitive Regression

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

Issue 4, October 2014

Probabilistic Reframing for Cost-Sensitive Regression

MDL4BMF: Minimum Description Length for Boolean Matrix Factorization

Feature Selection for Social Media Data

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

Discovering Social Circles in Directed Graphs

Random Projections for Linear Support Vector Machines

Reconstructing Graphs from Neighborhood Data

Issue 3, June 2014

Issue 2, June 2014

Issue 1(Casin special issue), February 2014

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Probabilistic Reframing for Cost-Sensitive Regression

Content Provider	ACM Digital Library
Author	Hern´ndez-orallo, José
Copyright Year	2014
Abstract	Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject). $\textit{Global}$ reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a $\textit{local}$ (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output $\textit{and}$ a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a $\textit{normal}$ conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight “enrichment” methods that produce good estimates of the conditional variance, which are used by the $\textit{probabilistic}$ (local) reframing methods. We apply these methods to some very common families of cost-sensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.
Starting Page	1
Ending Page	55
Page Count	55
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2641758
Volume Number	8
Issue Number	4
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cost-sensitive regression Asymmetric loss Calibration Conditional density estimation Reframing Reliability estimation in regression
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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