American Journal of Artificial Intelligence


Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications

This paper presents the design of dominance relations to reduce the space traversed in machine learning for solving two applications in financial trading and real-time multimedia. A machine-learning algorithm designed for an application with a huge search space will need to perform an efficient traversal of the space during learning. It will be more effective if it employs a powerful pruning mechanism to eliminate suboptimal candidates before using them in the learning algorithm. In our approach, we present dominance relations for pruning subspaces with suboptimal kernels that are otherwise evaluated in learning, where kernels represent the statistical quality, average density, or probability of solutions in a subspace. Specifically, when one subspace dominates another by a dominance relation, we can prune the latter and guarantee without searching both that the kernel of the latter cannot be better than that of the first. As a result, a significant portion of the search space will be pruned by those non- dominated subspaces during learning. In the financial trading application studied, we use mean reversion as our strategy for learning the set of promising stocks and Pareto-optimality as our dominance relation to reduce the space evaluated in learning. In the multimedia application, we propose a dominance relation using an axiom from our past work to approximate the subspace of perceptual qualities within an error threshold. The pruning mechanism allows the learning of the mapping from controls to perceptual qualities while eliminating the evaluation of all those mappings that are within the error thresholds. In both cases, we can harness the complexity of machine learning by reducing the candidate space evaluated.

Kernels, Dominance Relations, Machine Learning, Financial Trading, Mean Reversion, Real-time Multimedia, Perceptual Quality

Benjamin Wan-Sang Wah. (2023). Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications. American Journal of Artificial Intelligence, 6(2), 36-47.

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. E. Fama, “Random walks in stock market prices,” Financial Analysis Journal, vol. 21, no. 5, pp. 55-59, Sep.-Oct. 1965.
2. Y. LeCun, J. Denker, and S. Solla, “Optimal brain damage,” in Advances in Neural Information Processing Systems (D. Touretzky, ed.), vol. 2, Morgan-Kaufmann, 1989.
3. D. Blalock, J. J. G. Oritz, J. Frankle, and J. Guggag, “What is the state of neural network pruning,” in Proc. of the 3rd MLSys Conference, 2020.
4. L. A. Breslow and D. W. Aha, “Simplifying decision trees: A survey,” The Knowledge Engineering Review, vol. 12, no. 1, pp. 1-47, 1997.
5. G. T. Fechner, E. G. Boring, and D. H. Howes, Elements of Psychophysics [Elemente der Psychophysik (Translated by H. E. Adler)]. Holt, Rinehart and Winston, 1966 [First published, 1860].
6. B. W. Wah, “Using kernels to harness the complexity of big data applications,” Int’l Journal on Artificial Intelligence Tools, vol. 31, no. 3, pp. 2241006-1-11, 2022.
7. H.-W. Shen and A.-L. Barabasi, “Collective credit allocation in science,” Proc. National Academy of Sciences, vol. 111, no. 34, pp. 12325-12330, 2014.
8. S. Milgram, “The small-world problem,” Psychology Today, vol. 1, no. 1, pp. 61-67, 1967.
9. J. Huang, W.-Q. Wang, H.-W. Shen, G. Li, and X.-Q. Cheng, “Temporal scaling in information propagation,” Scientific Reports, vol. 4, no. 5334, pp. 1-6, 2014.
10. Z. Ji, Y. Pang, and X. Li, “Relevance preserving projection and ranking for web image search reranking,” IEEE Trans. on Image Processing, vol. 24, no. 11, pp. 4137-4147, 2015.
11. X. Yan, J. Guo, Y. Lan, J. Xu, and X. Cheng, “A probabilistic model for bursty topic discovery in microblogs,” in Proc. 29th AAAI Conference, (Austin, TX), 2015.
12. A. Jouglet and J. Carlier, “Dominance rules in combinatorial optimization problems,” European J. of Operational Research, vol. 212, no. 3, pp. 433-444, Aug. 2011.
13. J. Yang and J. Leskovec, “Overlapping community detection at scale: A nonnegative matrix factorization approach,” in Proc. ACM Int’l Conf. on Web Search and Data Mining (WSDM), (Rome, Italy), 2013.
14. Z. Wei, X. Liu, F. Li, S. Shang, X. Du, and J.-R. Wen, “Matrix sketching over sliding windows,” in Proc. ACM SIGMOD, (San Francisco, CA), 2016.
15. B. Li and S. C. H. Hoi, “Online portfolio selection: A survey,” ACM Computing Surveys, vol. 46, no. 3, p. 35, 2014.
16. A. Jouglet and J. Carlier, “Dominance rules in combinatorial optimization problems,” European J. of Operational Research, vol. 212, no. 3, pp. 433-444, 2011.
17. C.-L. Hwang and A. S. M. Masud, Multiple Objective Decision Making-Methods and Applications: A State- of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, No. 164, Springer-Verlag, 1979.
18. K. Deb, “Multi-objective optimization,” in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 403-449, Springer US, 2014.
19. N. Jegadeesh, “Evidence of predictable behavior of security returns,” Journal of Finance, vol. 45, pp. 881- 898, 1990.
20. S. Stigler, “Regression toward the mean, historically considered,” Statistical Methods in Medical Research, vol. 6, no. 2, pp. 103-114, 1997.
21. InteractiveBrokers, “Pricing structure,” Nov., 2022. home.php.
22. B. Li, P. Zhao, S. C. H. Hoi, and V. Gopalkrishnan, “PAMR: Passive aggressive mean reversion strategy for portfolio selection,” Machine Learning, vol. 87, no. 2, pp. 221-258, 2012.
23. J. Ferwerda, “Psychophysics 101: how to run perception experiments in computer graphics,” in ACM SIGGRAPH 2008 classes, (Los Angeles, CA), p. 87, 2008.
24. J. X. Xu and B. W. Wah, “Optimizing the perceptual quality of real-time multimedia applications,” IEEE Multimedia, vol. 22, no. 4, pp. 14-28, Oct-Dec 2015.
25. J. X. Xu and B. W. Wah, “Consistent synchronization of action order with noticeable dealy in online games,” ACM Trans. on Multimedia Computing, Communications, and Applications, vol. 8, no. 1, Jan. 2017.
26. X. Xu and B. W. Wah, “Optimality of greedy algorithm for generating just-noticeable difference surfaces,” IEEE Trans. on Multimedia, vol. 18, no. 7, pp. 1330-1337, July 2016.