NDLI: Yield Modeling with Rule Ensembles

Content Provider	IEEE Xplore Digital Library
Author	Seni, G. Yang, E. Akar, S.
Copyright Year	2007
Description	Author affiliation: PDF Solutions, San Jose (Seni, G.; Yang, E.; Akar, S.)
Abstract	In this paper we introduce the application of a new statistical modeling algorithm called rule ensembles to the problem of yield-loss characterization. Yield loss modeling is viewed as a regression or classification problem, and a model is constructed as a linear combination of simple rules derived from the data. These rule ensembles have been shown to produce predictive models competitive with the best methods. In addition to their high accuracy, however, these rules are easy to understand. Similarly, the degree of relevance of each rule, and its respective variables, can be assessed. The algorithm also provides methodology for automatically identifying those variables that are involved in interactions with other variables, and the strength and degrees of those interactions. To illustrate the interpretation advantages of the method, an analysis on semiconductor manufacturing data is provided.
Starting Page	228
Ending Page	233
File Size	403557
Page Count	6
File Format	PDF
ISBN	1424406528
DOI	10.1109/ASMC.2007.375099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-11
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Predictive models Input variables Semiconductor device manufacture Semiconductor device modeling Regression tree analysis Classification tree analysis Boosting Data analysis Training data ensembles Yield-loss characterization decision trees regression classification predictive learning
Content Type	Text
Resource Type	Article

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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of decision trees for integrated circuit yield improvement

Sample-Efficient Regression Trees (SERT) for Semiconductor Yield Loss Analysis

Semi-optimal hierarchical regression models and ANNs

Cascade Generalization with Classification and Model Trees

Data mining with ensembles of fuzzy decision trees

Meta-evolutionary ensembles

Ensembles of cascading trees

Predicting car purchase intent using data mining approach

Fast image interpolation with decision tree

Yield Modeling with Rule Ensembles

Similar Documents

Application of decision trees for integrated circuit yield improvement

Sample-Efficient Regression Trees (SERT) for Semiconductor Yield Loss Analysis

Semi-optimal hierarchical regression models and ANNs

Cascade Generalization with Classification and Model Trees

Data mining with ensembles of fuzzy decision trees

Meta-evolutionary ensembles

Ensembles of cascading trees

Predicting car purchase intent using data mining approach

Fast image interpolation with decision tree

Yield Modeling with Rule Ensembles