New classification algorithms for developing online program recommendation systems

Thomas Meller; Fuhua Lin; Eric Wang; Chunsheng Yang

doi:10.1109/eLmL.2009.19

New classification algorithms for developing online program recommendation systems

Thomas Meller, Fuhua Lin, Eric Wang, Chunsheng Yang

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper presents two novel nearestneighbor- like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naïve Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearestneighbor- like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

Original language	English
Title of host publication	Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009
Pages	67-72
Number of pages	6
DOIs	https://doi.org/10.1109/eLmL.2009.19
Publication status	Published - 2009
Event	International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009 - Cancun, Mexico Duration: 1 Feb. 2009 → 7 Feb. 2009

Publication series

Name	Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009

Conference

Conference	International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009
Country/Territory	Mexico
City	Cancun
Period	1/02/09 → 7/02/09

Keywords

Classification algorithms
Data mining
Program planning
Recommendation systems

Access to Document

10.1109/eLmL.2009.19

Cite this

Meller, T., Lin, F., Wang, E., & Yang, C. (2009). New classification algorithms for developing online program recommendation systems. In Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009 (pp. 67-72). Article 4782605 (Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009). https://doi.org/10.1109/eLmL.2009.19

@inproceedings{8afab701acf243c6b1b21857219024f0,

title = "New classification algorithms for developing online program recommendation systems",

abstract = "This paper presents two novel nearestneighbor- like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Na{\"i}ve Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearestneighbor- like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.",

keywords = "Classification algorithms, Data mining, Program planning, Recommendation systems",

author = "Thomas Meller and Fuhua Lin and Eric Wang and Chunsheng Yang",

year = "2009",

doi = "10.1109/eLmL.2009.19",

language = "English",

isbn = "9780769535289",

series = "Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009",

pages = "67--72",

booktitle = "Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009",

note = "International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009 ; Conference date: 01-02-2009 Through 07-02-2009",

}

Meller, T, Lin, F , Wang, E & Yang, C 2009, New classification algorithms for developing online program recommendation systems. in Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009., 4782605, Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009, pp. 67-72, International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009, Cancun, Mexico, 1/02/09. https://doi.org/10.1109/eLmL.2009.19

New classification algorithms for developing online program recommendation systems. / Meller, Thomas; Lin, Fuhua ; Wang, Eric et al.
Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009. 2009. p. 67-72 4782605 (Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009).

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

TY - GEN

T1 - New classification algorithms for developing online program recommendation systems

AU - Meller, Thomas

AU - Lin, Fuhua

AU - Wang, Eric

AU - Yang, Chunsheng

PY - 2009

Y1 - 2009

N2 - This paper presents two novel nearestneighbor- like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naïve Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearestneighbor- like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

AB - This paper presents two novel nearestneighbor- like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naïve Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearestneighbor- like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

KW - Classification algorithms

KW - Data mining

KW - Program planning

KW - Recommendation systems

UR - http://www.scopus.com/inward/record.url?scp=63849292104&partnerID=8YFLogxK

U2 - 10.1109/eLmL.2009.19

DO - 10.1109/eLmL.2009.19

M3 - Published Conference contribution

AN - SCOPUS:63849292104

SN - 9780769535289

T3 - Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009

SP - 67

EP - 72

BT - Proceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009

T2 - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009

Y2 - 1 February 2009 through 7 February 2009

ER -

New classification algorithms for developing online program recommendation systems

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