An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm

YU Lasheng; WU Xu; YANG Yu

doi:10.1049/cje.2018.06.006

Volume 28 Issue 1

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2019 > 28(1): 21-28

YU Lasheng, WU Xu, YANG Yu, “An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm,” Chinese Journal of Electronics, vol. 28, no. 1, pp. 21-28, 2019, doi: 10.1049/cje.2018.06.006

Citation:

YU Lasheng, WU Xu, YANG Yu, “An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm,” Chinese Journal of Electronics, vol. 28, no. 1, pp. 21-28, 2019, doi: 10.1049/cje.2018.06.006

Citation:

PDF( 1204 KB)

An Online Education Data Classification Model Based on Tr_MAdaBoost Algorithm

doi: 10.1049/cje.2018.06.006

School of Information Science & Engineering, Central South University, Changsha 410086, China

Received Date: 2017-02-22
Rev Recd Date: 2017-08-28
Publish Date: 2019-01-10

Abstract

Abstract

With the rapid development of network information technology and the wide application of smart phones, tablet PCs and other mobile terminals, online education plays an increasingly important role in social life. This article focuses on mining useful data from the massive online education data, by using transfer learning, relying on Hadoop, to construct Online education data classification framework (OEDCF), and design an algorithm Tr_MAdaBoost. This algorithm overcomes the traditional classification algorithms in which the required data must be restricted to independent and identically distributed data, since online education using this new algorithm can achieve the correct classification even it has different data distribution. At the same time, with the help of Hadoop's parallel processing architecture, OEDCF can greatly enhance the efficiency of data processing, create favorable conditions for learning analysis, and promote personalized learning and other activities of big data era.
- Transfer learning,
- Online education,
- OEDCF,
- AdaBoost algorithm,
- Tr_MAdaBoost algorithm

FullText(HTML)

References(19)

References

Dai W, "Instance-based and feature-based transfer learning", Diss, Shanghai Jiao Tong University, pp.4-5, 2009. (In Chinese)

Dai W, Yang Q, Xue G R, et al., "Boosting for transfer learning", Proceedings of the 24th International Conference on Machine learning, ACM, Vol.238, pp.193-200, 2007.

Wei F, Zhang J, Yan C, et al., "FSFP:Transfer learning from long texts to the short", Applied Mathematics & Information Sciences, Vol.8, No.4, pp.2033-2040, 2014.

Kuzborskij I, Orabona F, Caputo B, et al., "Scalable greedy algorithms for transfer learning", Computer Vision & Image Understanding, Vol.156, pp.174-185, 2016.

Wei X, Zhou S G, Guan J H, et al., "Classification in networked data:A survey", Pattern Recognition & Artificial Intelligence, Vol.24, No.4, pp.527-537, 2011.

Meesookho C, Narayanan S, Raghavendra C S, et al., "Collaborative classification applications in sensor networks", Sensor Array and Multichannel Signal Processing Workshop Proceedings, IEEE, pp.370-374, 2002.

Shang R, Zhang Z, Jiao L, et al., "Global discriminative-based nonnegative spectral clustering", Pattern Recognition, Vol.55, No.C, pp.172-182, 2016.

Shang R, Zhang Z, Jiao L, et al., "Self-representation based dual-graph regularized feature selection clustering", Neurocomputing, Vol.171, pp.1242-1253, 2016.

Shang R, Wang W, Stolkin R, et al., "Non-negative spectral learning and sparse regression-based dual-graph regularized feature selection", IEEE Transactions on Cybernetics, Vol.48, No.2, pp.793-806, 2018.

Wang C, "A geometric framework for transfer learning using manifold alignment", Dissertations & Theses, University of Massachuseths Amherst, 2010.

Yao Y and Doretto G, "Boosting for transfer learning with multiple sources", IEEE Conference on Computer Vision & Pattern Recognition, pp.1855-1862, 2010.

Kandaswamy C, Silva L M, Alexandre L A, et al., "Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders", IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp.1380-1387, 2014.

Chaturvedi I, Ong Y S, Arumugam R V, et al., "Deep transfer learning for classification of time-delayed Gaussian networks", Signal Processing, Vol.110, No.C, pp.250-262, 2015.

Fang M, Guo Y, Zhang X, et al., "Multi-source transfer learning based on label shared subspace", Pattern Recognition Letters, Vol.51, No.C, pp.101-106, 2015.

Nguyen T T, Silander T, Li Z, et al., "Scalable transfer learning in heterogeneous, dynamic environments", Artificial Intelligence, Vol.247, pp.70-94, 2017.

Y Freund and R Schapire, "A decision-theoretic generalization of n-line learning and an application to boosting", Helvetica ChimicaActa, Vol.55, No.7, pp.119-139, 2010.

Wu S and Nagahashi H, "Analysis of generalization ability for different adaboost variants based on classification and regression Trees", Journal of Electrical & Computer Engineering, Vol.2015, pp.1-17, 2015.

Demirkır C and Sankur B, "Face detection using look-up table based gentle adaboost", Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, pp.339-345, 2005.

Sam K T and Tian X L, "Vehicle logo recognition using modest adaboost and radial tchebichef moments", International Proceedings of Computer Science & Information Tech, Vol.25, pp.91-95, 2012.

Relative Articles

Supplements(0)

Cited By

Proportional views