Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

Author(s): Narina Thakur*, Deepti Mehrotra, Abhay Bansal, Manju Bala

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 13 , Issue 2 , 2020

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Graphical Abstract:


Abstract:

Objectives: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision.

Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient.

Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab.

Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

Keywords: Learning object repository, support vector machine, landmark model, tilted time window model, precision, recall.

[1]
S. Zelikovitz, W.W. Cohen, and H. Hirsh, "Extending WHIRL with background knowledge for improved text classification", Inf. Retrieval, vol. 10, no. 1, pp. 35-67, 2007.
[2]
H-L. Yang, S-C. Li, F-C. Zhang, and Z-F. Wu, "Method and grid workflow on water consumption forecasting of Ziyang city based on support vector machine trained by genetic algorithm", Recent Pat. Eng., vol. 4, no. 1, 2010.
[http://dx.doi.org/10.2174/187221210790244712]
[3]
J.A.K. Suykens, and J. Vandewalle, "Least squares support vector machine classifiers", Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
[4]
S. Gong, S.J. McKenna, and A. Psarrou, From images to face recognition.Image Processing, Imperial College Press, 1999
[5]
C. Cortes, and V. Vapnik, "Support-vector networks", Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
[6]
S.V. Sheela, and P.A. Vijaya, "Non-linear classification for iris patterns", In: Multimedia Computing and Systems (ICMCS), 2011 International Conference on, 2011, pp. 1-5.
[7]
"J. S.-Taylor and N. Cristianini", Kernel methods for pattern analysis.Cambridge University Press, . 2004
[8]
B. Koo, S. La, N.W. Cho, and Y. Yu, "Using support vector machines to classify building elements for checking the semantic integrity of building information models", Autom. Construct., vol. 98, pp. 183-194, 2019.
[9]
M. Pontil, and A. Verri, "Support vector machines for 3D object recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 6, pp. 637-646, 1998.
[10]
C. Cortes, and V. Vapnik, "Support-vector networks", Mach. Learn., vol. 20, pp. 3273-3297, 1995.
[11]
H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, and V. Vapnik, Support vector regression machines., Adv. Neur. Inform. Process Syst, 1997, pp. 155-161.
[12]
C.J. Burges, "A tutorial on support vector machines for pattern recognition", Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121-167, 1998.
[13]
W. Chu, and S.S. Keerthi, "New approaches to support vector ordinal regression", In: Proceedings of the 22nd International Conference on Machine learning. Bonn, Germany, 2005, pp. 145-152.
[14]
A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components", IEEE Trans. Pattern Anal. Mach. Intell., vol. 1, no. 4, pp. 349-361, 2001.
[15]
M. Deypir, M.H. Sadreddini, and S. Hashemi, "Towards a variable size sliding window model for frequent item set mining over data streams", Comput. Ind. Eng., vol. 63, no. 1, pp. 161-172, 2012.
[16]
C.H. Lin, D.Y. Chiu, Y.H. Wu, and A.L. Chen, "Mining frequent item sets from data streams with a time-sensitive sliding window", In: Proceedings of the 2005 SIAM International Conference on Data Mining, 2005, pp. 68-79.
[17]
C. Schuldt, I. Laptev, and B. Caputo, "Recognizing human actions: A local SVM approach", In: Proceedings of the 17th International Conference on Pattern Recognition.2004. ICPR 2004. Cambridge, UK,, 2004, Vol. 3, pp. 32-36.
[18]
"Z. Lin, M. Chen and Y. Ma, “The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices”, arXiv preprint arXiv:", 1009.5055. 2010.
[19]
H. Everett III, "Generalized Lagrange multiplier method for solving problems of optimum allocation of resources", Oper. Res., vol. 11, no. 3, pp. 399-417, 1963.
[20]
T. Joachims, "Text categorization with support vector machines: Learning with many relevant features. In: European Conference On Machine Learning", Springer, Berlin, Heidelberg, 1998, pp. 137-142.
[21]
R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification.Edition Wiley interscience. New York., 2001
[22]
E. Bron, M. Smits, J. van Swieten, W. Niessen, and S. Klein, "Feature selection based on SVM significance maps for classification of dementia", In: International Workshop on Machine Learning in Medical Imaging. SpringerLink, 2014, pp. 272-279
[23]
C. Giannella, J. Hany, J. Peiz, X. Yany, and P.S. Yu, "Mining frequent patterns in data streams at multiple time granularities", Next Gen. Data Min., vol. 212, pp. 191-212, 2003.
[24]
N. Jiang, and L. Gruenwald, "Research issues in data stream association rule mining", ACM Sigmod Record., vol. 35, no. 1, pp. 14-19, 2006.
[25]
P.S. Tsai, "Mining frequent item sets in data streams using the weighted sliding window model", Expert Syst. Appl., vol. 36, no. 9, pp. 11617-11625, 2009.
[26]
Y.D. Cai, D. Clutter, G. Pape, J. Han, M. Welge, and L. Auvil, "MAIDS: Mining alarming incidents from data streams", In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. Paris, France, 2004, pp. 919-920.
[27]
(a)N.Y. Yen, T.K. Shih, L.R. Chao, and Q. Jin, "Ranking metrics and search guidance for learning object repository", IEEE Trans. Learn. Technol., vol. 3, no. 3, pp. 250-264, 2010.(b)X. Peipei, L. Zhang, and L. Fanzhang, "Learning similarity with cosine similarity ensemble", Inf. Sci., vol. 307, pp. 39-52, 2015.
[28]
P. Xia, L. Zhang, and F. Li, "Learning similarity with cosine similarity ensemble", Inf. Sci., vol. 307, pp. 39-52, 2015.
[29]
T. Xiaoling, W. Yong, W. Yi, and L. Ye, Network traffic classification based on multi-classifier selective ensemble.Rec. Adv. Elec. Electron. Eng., Vol. 8, no. 2, 2015.
[30]
D. Hardin, I. Tsamardinos, and C.F. Aliferis, "A theoretical characterization of linear SVM-based feature selection", In: Proceedings of the twenty-first international conference on Machine learning.Banff, Alberta, Canada, 2004
[31]
S.R. Gunn, "Support vector machines for classification and regression", ISIS Technical Report, vol. 14, no. 1, pp. 5-16, 1998.
[32]
M. Bala, and R.K. Agrawal, Optimal decision tree based multi-class support vector machine.Informatica,Vol. 35, no. 2, , 2011
[33]
C-W. Hsu, C-C. Chang, and C-J. Lin, A practical guide to support vector classification., pp. 1-16. 2003
[34]
"NCBI PubMed dataset,", http://www.ncbi.nlm.nih.gov/pubmed
[35]
J.H. Hayes, A. Dekhtyar, and J. Osborne, "Improving requirements tracing via information retrieval", In: Requirements Engineering Conference.2003. Proceedings. 11th IEEE International Monterey Bay, CA, USA , 2003, pp. 138-147.


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Article Details

VOLUME: 13
ISSUE: 2
Year: 2020
Page: [153 - 164]
Pages: 12
DOI: 10.2174/2213111607666190215120017
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