NDLI: Classification of students by using an incremental ensemble of classifiers

Content Provider	IEEE Xplore Digital Library
Author	Ade, Roshani Deshmukh, P. R.
Copyright Year	2014
Description	Author affiliation: Dept. of Computer Engineering, Sipna College of Engineering and Technology, Sant Gadge Baba Amravati University, Maharashtra, India (Ade, Roshani; Deshmukh, P. R.)
Abstract	The amount of students data in the education system databases is growing day by day, so the knowledge taken out from these data need to be updated continuously. The training set of the supervised learning algorithms contains student's score in the test. Incremental learning ability is further significant for machine learning methodologies as student's data and the information is increasing. Against to the classical batch learning algorithm, incremental learning algorithm tries to forget unrelated information while training new instances. Now a days, combination of a classifiers is a novel concept for overall progress in the classification result. Therefore, an incremental ensemble of two classifiers namely Naïve Bayes, K-Star using majority voting scheme is proposed. The large scale comparison of a proposed ensemble technique by using different voting scheme with the state-of the art algorithm on the student's data set has been done. The experimental results shown high accuracy for the proposed ensemble for the student's classification. High accuracy was also achieved for the majority voting scheme as compared to other voting scheme.
Starting Page	1
Ending Page	5
File Size	232131
Page Count	5
File Format	PDF
ISBN	9781479968954
e-ISBN	9781479968961
DOI	10.1109/ICRITO.2014.7014666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Accuracy Machine learning algorithms Incremental learning Neural networks Education system Ensemble Prediction algorithms Voting scheme Classification algorithms
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

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