NDLI: Experiments on probabilistic approximations

Content Provider	IEEE Xplore Digital Library
Author	Clark, P.G. Grzymala-Busse, J.W.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA (Clark, P.G.; Grzymala-Busse, J.W.)
Abstract	Recently much attention has been paid to probabilistic (parameterized) approximations that are generalizations of ordinary lower and upper approximations known from rough set theory. The first objective of this paper is to compare the quality of such approximations and ordinary, lower and upper approximations. The second objective is to show that the number of distinct probabilistic approximations is quite limited. In our experiments we used six real-life data sets. Obviously, inconsistent data sets are required for such experiments, so the level of consistency in all data sets used for our experiments was decreased to enhance our experiments. Our main result is rather pessimistic: probabilistic approximations, different from ordinary lower or upper approximations, were better than ordinary approximations for only two out of these six data sets.
Starting Page	144
Ending Page	149
File Size	224992
Page Count	6
File Format	PDF
ISBN	9781457703720
e-ISBN	9781457703713
DOI	10.1109/GRC.2011.6122583
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-08
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	rule induction algorithm MLEM2 Iris Error analysis parameterized approximations Glass probabilistic approximations Probabilistic logic Set theory Approximation methods Data mining rough set theory Iris recognition
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

Experiments on rule induction from incomplete data using three probabilistic approximations

A Comparison of global and local probabilistic approximations in mining data with many missing attribute values

A comparison of positive, boundary, and possible rules using the MLEM2 rule induction algorithm

Mining Inconsistent Data with the Bagged MLEM2 Rule Induction Algorithm

Mining data with numerical attributes and missing attribute values — A rough set approach

Mining incomplete data with many attribute-concept values and "do not care" conditions

Rough set and CART approaches to mining incomplete data

Mining incomplete data with lost values and attribute-concept values

Faster rule induction algorithms using rough set theory

Experiments on probabilistic approximations

Similar Documents

Experiments on rule induction from incomplete data using three probabilistic approximations

A Comparison of global and local probabilistic approximations in mining data with many missing attribute values

A comparison of positive, boundary, and possible rules using the MLEM2 rule induction algorithm

Mining Inconsistent Data with the Bagged MLEM2 Rule Induction Algorithm

Mining data with numerical attributes and missing attribute values — A rough set approach

Mining incomplete data with many attribute-concept values and "do not care" conditions

Rough set and CART approaches to mining incomplete data

Mining incomplete data with lost values and attribute-concept values

Faster rule induction algorithms using rough set theory

Experiments on probabilistic approximations