NDLI: Evolutionary automated classification

Content Provider	IEEE Xplore Digital Library
Author	Luchian, S. Luchian, H. Petriuc, M.
Copyright Year	1994
Description	Author affiliation: Fac. of Comput. Sci., Iasi Univ., Romania (Luchian, S.; Luchian, H.)
Abstract	The problem of classification can be stated as: "Given a set of a attributes and the characterization of n objects by means of a attribute values for each object, find an optimal classification (partition) of the n objects into classes". The first step in supervised classification is to find k, the number of classes into which the objects have to be split in order to obtain the optimum classification. The dynamic kernels algorithm searches for a classification which maximizes B [=/spl Sigma//sub j=1//sup k/n/sub j/d(G/sub j/,G), where d(G/sub j/,G) denotes the distance between the points G/sub j/ and G, G is the center of gravity of the n given points and G/sub j/ are the centers of gravity of the k classes]. Unfortunately, the maximum value of B is not the same for different values of k, so the Huygens theorem can only be used for comparing classifications of the given objects into a fixed number of classes. We propose an evolution program for this problem. It simultaneously searches for both the optimum number of classes and the optimal classification. So far, we have implemented our approach for the case of two attributes with continuous values. Straightforward adaptations are under way for allowing the user to input any reasonable number of (continuous or discrete) attributes.<>
Starting Page	585
Ending Page	588
File Size	334538
Page Count	4
File Format	PDF
ISBN	0780318994
DOI	10.1109/ICEC.1994.349994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological cells Gravity Computer science Automatic control Control engineering computing Kernel Heuristic algorithms Microwave integrated circuits Linear discriminant analysis Encoding
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 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

Iterative Kernel Discriminant Analysis Algorithm for Document Classification

Kernel Discriminant Learning for Ordinal Regression

Production genetic algorithms for automated hardware design through an evolutionary process

Feedforward/feedback control of discrete linear repetitive processes with disturbances applied to a material rolling process

A unified framework for computer-aided noise analysis of linear and nonlinear microwave circuits

Spectral Methods for Cancer Classification Using Microarray Data

Discriminant Isomap projection

Linear discriminant multiple kernel learning for multispectral image classification

Nonlinear dimensionality reduction for classification using kernel weighted subspace method

Evolutionary automated classification

Similar Documents

Iterative Kernel Discriminant Analysis Algorithm for Document Classification

Kernel Discriminant Learning for Ordinal Regression

Production genetic algorithms for automated hardware design through an evolutionary process

Feedforward/feedback control of discrete linear repetitive processes with disturbances applied to a material rolling process

A unified framework for computer-aided noise analysis of linear and nonlinear microwave circuits

Spectral Methods for Cancer Classification Using Microarray Data

Discriminant Isomap projection

Linear discriminant multiple kernel learning for multispectral image classification

Nonlinear dimensionality reduction for classification using kernel weighted subspace method

Evolutionary automated classification