Prediction Algorithms: A Study

Authors

  • S. Santha Subbulaxmi Research Scholar, Department of Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, India
  • G. Arumugam Professor & Head (Retired), Department of Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.3.1896

Keywords:

Prediction Algorithm, Regression Algorithms, Instance Based Algorithms, Decision Tree Algorithms, Bayesian Algorithms, Clustering Algorithms, Artificial Neural Network Algorithms, Ensemble Algorithms

Abstract

Prediction algorithms make a prognosis of the future in a scientific way by analysing the data. They are being applied successfully to the problems in various fields and find good solutions. The objective of this paper is to describe about prediction algorithms and present the literature growth on it. It details the prediction process. It outlines the different types of prediction algorithms and the relevant publications on it. The paper summarizes the advantages & disadvantages of the prediction algorithms and the challenges to be addressed in the prediction field.

References

E. G. Larsson and Y. Selén, ―Linear regression with a sparse parameter vector‖, IEEE Transactions on Signal Processing, Vol. 55, No. 2, pp. 451–460, 2007.

J. Wu and Q. Huo, ―A study of minimum classification error (MCE) linear regression for supervised adaptation of MCE-trained continuous-density hidden Markov models‖, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, No. 2, pp. 478–488, 2007.

J. Zhang and J. Jiang, ―Rank-optimized logistic matrix regression toward improved matrix data classification‖, Neural computation, Vol. 30, No. 2, pp. 505–525, 2018.

C. Li and T.-W. Chiang, ―Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets‖, IEEE Transactions on Fuzzy Systems, Vol. 21, No. 3, pp. 567–584, 2013.

S. Gong, Y. Gao, H. Shi, and G. Zhao, ―A practical MGA-ARIMA model for forecasting real-time dynamic rain-induced attenuation‖, Radio Science, Vol. 48, No. 3, pp. 208–225, 2013.

C. Fu, ―Business Valuation Based on Intellectual Capital: A Hierarchical Clustering-MARS Approach‖, in Management and Service Science (MASS), 2011 International Conference on, 2011, pp. 1–6.

Crino S, Brown DE. ―Global optimization with multivariate adaptive regression splines‖. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). Vol. 37, No.2, pp.333-340, Apr. 2007.

J. E. Goin, ―Classification bias of the k-nearest neighbor algorithm‖, IEEE transactions on pattern analysis and machine intelligence, No. 3, pp. 379–381, 1984.

J. M. Keller, M. R. Gray, and J. A. Givens, ―A fuzzy k-nearest neighbor algorithm‖, IEEE transactions on systems, man, and cybernetics, No. 4, pp. 580–585, 1985.

S. R. Krishnan, C. S. Seelamantula, and P. Chakravarti, ―Spatially Adaptive Kernel Regression Using Risk Estimation‖, IEEE Signal Processing Letters, Vol. 21, No. 4, pp. 445–448, 2014.

H. Zhang and Z. Jiang, ―Constrained kernel regression for pose estimation‖, Electronics Letters, Vol. 50, No. 2, pp. 77–79, 2014.

R. K. Sevakula and N. K. Verma, ―Compounding General Purpose Membership Functions for Fuzzy Support Vector Machine Under Noisy Environment‖, IEEE Transactions on Fuzzy Systems, Vol. 25, No. 6, pp. 1446–1459, 2017.

X. Ma, Q. Ye, and H. Yan, ―L2P-Norm Distance Twin Support Vector Machine‖, IEEE Access, Vol. 5, pp. 23473–23483, 2017.

A. Suárez and J. F. Lutsko, ―Globally optimal fuzzy decision trees for classification and regression‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 12, pp. 1297–1311, 1999.

A. Painsky and S. Rosset, ―Cross-validated variable selection in tree-based methods improves predictive performance‖, IEEE transactions on pattern analysis and machine intelligence, Vol. 39, No. 11, pp. 2142–2153, 2017.

N. R. Pal and S. Chakraborty, ―Fuzzy rule extraction from ID3-type decision trees for real data‖, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 31, No. 5, pp. 745–754, 2001.

K. J. Cios and N. Liu, ―A machine learning method for generation of a neural network architecture: A continuous ID3 algorithm‖, IEEE Transactions on Neural Networks, Vol. 3, No. 2, pp. 280–291, 1992.

Z.-H. Zhou and Y. Jiang, ―NeC4. 5: neural ensemble based C4. 5‖, IEEE Transactions on Knowledge & Data Engineering, No. 6, pp. 770–773, 2004.

Y. Yang and W. Chen, ―Taiga: performance optimization of the C4. 5 decision tree construction algorithm‖, Tsinghua Science and Technology, Vol. 21, No. 4, pp. 415–425, 2016.

L. Kuncheva and Z. Hoare, ―Error-dependency relationships for the Naïve Bayes classifier with binary features‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 4, pp. 735–740, 2008.

L. Jiang, H. Zhang, and Z. Cai, ―A novel Bayes model: Hidden naive Bayes‖, IEEE Transactions on knowledge and data engineering, Vol. 21, No. 10, pp. 1361–1371, 2009.

L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, ―Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data‖, IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No. 11, p. 2269, 2016.

K. Kampa, E. Hasanbelliu, J. T. Cobb, J. C. Principe, and K. C. Slatton, ―Deformable Bayesian network: A robust framework for underwater sensor fusion‖, IEEE Journal of Oceanic Engineering, Vol. 37, No. 2, pp. 166–184, 2012.

S. Chen, X. Yang, and Y. Tian, ―Discriminative hierarchical K-means tree for large-scale image classification‖, IEEE transactions on neural networks and learning systems, Vol. 26, No. 9, pp. 2200–2205, 2015.

J. Yang and J. Wang, ―Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis‖, Journal of Systems Engineering and Electronics, Vol. 28, No. 2, pp. 374–384, 2017.

P. Govindasamy and R. Dillibabu, ―Maximum likelihood estimation and optimisation of parameters of software reliability models using evolutionary optimisation techniques‖, 2018.

L. Lu, H.-C. Wu, K. Yan, and S. S. Iyengar, ―Robust expectation-maximization algorithm for multiple wideband acoustic source localization in the presence of nonuniform noise variances‖, IEEE Sensors Journal, Vol. 11, No. 3, pp. 536–544, 2011.

S. Rajasekaran, ―Efficient parallel hierarchical clustering algorithms‖, IEEE transactions on parallel and distributed systems, Vol. 16, No. 6, pp. 497–502, 2005.

Y. Jeon, J. Yoo, J. Lee, and S. Yoon, ―Nc-link: A new linkage method for efficient hierarchical clustering of large-scale data‖, IEEE Access, Vol. 5, pp. 5594–5608, 2017.

S. A. Cannas, ―Arithmetic perceptrons‖, Neural Computation, Vol. 7, No. 1, pp. 173–181, 1995.

A. Navia-Vazquez and A. R. Figueiras-Vidal, ―Efficient block training of multilayer perceptrons‖, Neural computation, Vol. 12, No. 6, pp. 1429–1447, 2000.

S. Siu, S.-S. Yang, C.-M. Lee, and C.-L. Ho, ―Improving the back-propagation algorithm using evolutionary strategy‖, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 54, No. 2, pp. 171–175, 2007.

P. A. Mastorocostas, ―Resilient back propagation learning algorithm for recurrent fuzzy neural networks‖, Electronics Letters, Vol. 40, No. 1, pp. 57–58, 2004.

C. Li, X. Yu, T. Huang, G. Chen, and X. He, ―A generalized Hopfield network for nonsmooth constrained convex optimization: Lie derivative approach‖, IEEE transactions on neural networks and learning systems, Vol. 27, No. 2, pp. 308–321, 2016.

X. Liang, L. Wang, Y. Wang, and R. Wang, ―Dynamical behavior of delayed reaction–diffusion Hopfield neural networks driven by infinite dimensional Wiener processes‖, IEEE transactions on neural networks and learning systems, Vol. 27, No. 9, pp. 1816–1826, 2016.

P.-B. Zhang and Z.-X. Yang, ―A novel AdaBoost framework with robust threshold and structural optimization‖, IEEE transactions on cybernetics, Vol. 48, No. 1, pp. 64–76, 2018.

H. Yu and P. Moulin, ―Regularized Adaboost Learning for Identification of Time-Varying Content.‖, IEEE Trans. Information Forensics and Security, Vol. 9, No. 10, pp. 1606–1616, 2014.

N. Quadrianto and Z. Ghahramani, ―A very simple safe-Bayesian random forest‖, IEEE transactions on pattern analysis and machine intelligence, Vol. 37, No. 6, pp. 1297–1303, 2015.

M. V. Reddy and R. Sodhi, ―A Modified S-Transform and Random Forests-Based Power Quality Assessment Framework‖, IEEE Transactions on Instrumentation and Measurement, Vol. 67, No. 1, pp. 78–89, 2018.

M. H. Sherif, R. J. Gregor, and J. Lyman, ―Effects of load on myoelectric signals: the ARIMA representation‖, IEEE Transactions on Biomedical Engineering, No. 5, pp. 411–416, 1981.

D. Barber, D. Saad, and P. Sollich, ―Test error fluctuations in finite linear perceptrons‖, Neural computation, Vol. 7, No. 4, pp. 809–821, 1995.

Li J, Li X, Huang B, Zhao L.‖Hopfield neural network approach for supervised nonlinear spectral unmixing‖, IEEE Geoscience and Remote Sensing Letters, Vol.13, No.7, pp. 1002-1006, Jul. 2016.

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Published

05-11-2018

How to Cite

Santha Subbulaxmi, S., & Arumugam, G. (2018). Prediction Algorithms: A Study. Asian Journal of Computer Science and Technology, 7(3), 7–12. https://doi.org/10.51983/ajcst-2018.7.3.1896