NDLI: Customer return detection with features selection

Content Provider	IEEE Xplore Digital Library
Author	Bertoncelli, D. Caianiello, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Eng., Univ. of L'Aquila, L'Aquila, Italy (Bertoncelli, D.; Caianiello, P.)
Abstract	We address the semiconductor industry problem of detecting microchips that escape production tests but are returned by customers as non-functional. This problem deals with analyzing high dimensional unbalanced databases collecting only a very small number of customer return samples. We show how to construct a model for effectively discriminating, based on wafer probe test data, potential customer returns from other good chips at the cost of a low overkill, where a model is a pair consisting of a selected set of wafer probe tests with minimal redundancy and a 1-class-SVM (Support Vector Machine) with optimal kernel parameters. We report about an experimentation on real data from EWS (Electronic Wafer Sort) test and customer returns showing the capability of predicting customer returns at cost of a relatively low overkill.
Sponsorship	IEEE Comput. Soc.
Starting Page	268
Ending Page	269
File Size	246668
Page Count	2
File Format	PDF
ISBN	9781479945603
e-ISBN	9781479945580
DOI	10.1109/DDECS.2014.6868806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-23
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semiconductor device modeling Support vector machines Predictive models Kernel Probes Prediction algorithms Mutual information customer return semiconductor feature selection support vector machine
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	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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library 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

Feature selection based on mutual information

Anomaly Detection Using an Ensemble of Feature Models

Building comprehensible customer churn prediction models: A multiple kernel support vector machines approach

Feature Selection for Data Driven Prediction of Protein Model Quality

The Explanation of Support Vector Machine in Customer Churn Prediction

A novel support vector machine with its features weighted by mutual information

Important test selection for screening potential customer returns

Feature selection for facial expression recognition

Personalized modeling for drug concentration prediction using Support Vector Machine

Customer return detection with features selection

Similar Documents

Feature selection based on mutual information

Anomaly Detection Using an Ensemble of Feature Models

Building comprehensible customer churn prediction models: A multiple kernel support vector machines approach

Feature Selection for Data Driven Prediction of Protein Model Quality

The Explanation of Support Vector Machine in Customer Churn Prediction

A novel support vector machine with its features weighted by mutual information

Important test selection for screening potential customer returns

Feature selection for facial expression recognition

Personalized modeling for drug concentration prediction using Support Vector Machine

Customer return detection with features selection