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Reseach Article

Effective Classification using a small Training Set based on Discretization and Statistical Analysis

Published on May 2016 by Aishwarya B. Jadhav, V.s. Nandedkar
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 4
May 2016
Authors: Aishwarya B. Jadhav, V.s. Nandedkar
ddc67800-9319-40f0-bb26-86be97c93bcc

Aishwarya B. Jadhav, V.s. Nandedkar . Effective Classification using a small Training Set based on Discretization and Statistical Analysis. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 4 (May 2016), 5-8.

@article{
author = { Aishwarya B. Jadhav, V.s. Nandedkar },
title = { Effective Classification using a small Training Set based on Discretization and Statistical Analysis },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 4 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 5-8 },
numpages = 4,
url = { /proceedings/ncacit2016/number4/24717-3057/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Aishwarya B. Jadhav
%A V.s. Nandedkar
%T Effective Classification using a small Training Set based on Discretization and Statistical Analysis
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 4
%P 5-8
%D 2016
%I International Journal of Computer Applications
Abstract

In this paper, we depict the work with the issue of creating a quick and precise information order, gaining from little arrangement of records. The proposed methodology depends on the system of the alleged Logical Analysis of Information (LAD), however advanced with data got from measurable contemplations on the information. Various discrete streamlining issues are illuminated in the diverse strides of the system, yet their computational interest can be controlled. The precision of the proposed methodology is contrasted with that of the standard LAD calculation, of Support Vector Machines and of Label Propagation calculation on openly accessible datasets of the UCI storehouse.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Algorithms Data Mining Machine Learning Discrete Mathematics Optimization.