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

Benjamin Wan-Sang Wah

doi:doi:10.11648/j.ajai.20220602.12

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

Benjamin Wan-Sang Wah

Published in American Journal of Artificial Intelligence (Volume 6, Issue 2)

Received: 25 November 2022 Accepted: 26 December 2022 Published: 6 February 2023

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Abstract

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.

Published in	American Journal of Artificial Intelligence (Volume 6, Issue 2)
DOI	10.11648/j.ajai.20220602.12
Page(s)	36-47
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

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

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APA Style

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. https://doi.org/10.11648/j.ajai.20220602.12

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ACS Style

Benjamin Wan-Sang Wah. Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications. Am. J. Artif. Intell. 2023, 6(2), 36-47. doi: 10.11648/j.ajai.20220602.12

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AMA Style

Benjamin Wan-Sang Wah. Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications. Am J Artif Intell. 2023;6(2):36-47. doi: 10.11648/j.ajai.20220602.12

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@article{10.11648/j.ajai.20220602.12,
  author = {Benjamin Wan-Sang Wah},
  title = {Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications},
  journal = {American Journal of Artificial Intelligence},
  volume = {6},
  number = {2},
  pages = {36-47},
  doi = {10.11648/j.ajai.20220602.12},
  url = {https://doi.org/10.11648/j.ajai.20220602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20220602.12},
  abstract = {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.},
 year = {2023}
}

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TY  - JOUR
T1  - Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications
AU  - Benjamin Wan-Sang Wah
Y1  - 2023/02/06
PY  - 2023
N1  - https://doi.org/10.11648/j.ajai.20220602.12
DO  - 10.11648/j.ajai.20220602.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 36
EP  - 47
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20220602.12
AB  - 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.
VL  - 6
IS  - 2
ER  -

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Author Information

Benjamin Wan-Sang Wah

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

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Plain Text BibTeX RIS

APA Style

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. https://doi.org/10.11648/j.ajai.20220602.12

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ACS Style

Benjamin Wan-Sang Wah. Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications. Am. J. Artif. Intell. 2023, 6(2), 36-47. doi: 10.11648/j.ajai.20220602.12

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AMA Style

Benjamin Wan-Sang Wah. Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications. Am J Artif Intell. 2023;6(2):36-47. doi: 10.11648/j.ajai.20220602.12

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@article{10.11648/j.ajai.20220602.12,
  author = {Benjamin Wan-Sang Wah},
  title = {Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications},
  journal = {American Journal of Artificial Intelligence},
  volume = {6},
  number = {2},
  pages = {36-47},
  doi = {10.11648/j.ajai.20220602.12},
  url = {https://doi.org/10.11648/j.ajai.20220602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20220602.12},
  abstract = {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.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Dominance Pruning in Machine Learning for Solving Financial Trading and Real-Time Multimedia Applications
AU  - Benjamin Wan-Sang Wah
Y1  - 2023/02/06
PY  - 2023
N1  - https://doi.org/10.11648/j.ajai.20220602.12
DO  - 10.11648/j.ajai.20220602.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 36
EP  - 47
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20220602.12
AB  - 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.
VL  - 6
IS  - 2
ER  -

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