IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v9y2022i4p54-60.html
   My bibliography  Save this article

Decision Making and Machine Learning Algorithms’ Selection with Artificial Intelligent Rule-Based Expert System

Author

Listed:
  • Ismail Olaniyi MURAINA

    (Computer Science Department, College of Information and Technology Education, Lagos State University of Education, Lagos Nigeria)

  • Moses Adeolu AGOI

    (Computer Science Department, College of Information and Technology Education, Lagos State University of Education, Lagos Nigeria)

  • Benjamin Oghomena OMOROJOR

    (Computer Science Department, College of Information and Technology Education, Lagos State University of Education, Lagos Nigeria)

  • Akeem Ademola ADEDOKUN

    (Computer Science Department, College of Information and Technology Education, Lagos State University of Education, Lagos Nigeria)

  • Rasheed Olatunde AJETUNMOBI

    (Computer Science Department, College of Information and Technology Education, Lagos State University of Education, Lagos Nigeria)

Abstract

Everybody is confronted daily with cluster of decisions that must be appropriately taken in the process of making accurate decision; individuals are faced with and most often fall prey to series of common biases, fallacies, and many other decision making odds. In determining which algorithm to apply for analysis (with machine learning algorithms/models) open to critical steps to be taken and also highly depend on many factors ranging from the type of problem at hand, the condition to choose a model and to the expected outcomes. The study looks at how artificial intelligent approach with expert system would be helpful in making timely decision on which type of algorithm(s) is/are capable to be applied and implemented to have desired results. The study also uses VisiRule software to model series of successful channels to arrive at a good decision making means. The use of VisiRule (Artificial Intelligent Based Expert System) was employed to give directional path ways to the selection of appropriate algorithms from supervised and unsupervised machine learning to different classification methods, regression methods, clustering approaches, dimensionality reduction methods, and association rules. The outcome of this study demonstrates the easy way through paths to select relevant and most appropriate model or algorithm that best fit the analysis at hand with detailed explanation of each alternative option. The use of VisiRule software has proven the easy way to achieve decision making problems without any codes requirement for such actions. Decision making challenges could be resolved by just implementing artificial intelligent rule-based expert system which require less time, coding free, and highly achievable accurate outcomes.

Suggested Citation

  • Ismail Olaniyi MURAINA & Moses Adeolu AGOI & Benjamin Oghomena OMOROJOR & Akeem Ademola ADEDOKUN & Rasheed Olatunde AJETUNMOBI, 2022. "Decision Making and Machine Learning Algorithms’ Selection with Artificial Intelligent Rule-Based Expert System," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 9(4), pages 54-60, April.
  • Handle: RePEc:bjc:journl:v:9:y:2022:i:4:p:54-60
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-9-issue-4/54-60.pdf
    Download Restriction: no

    File URL: https://www.rsisinternational.org/virtual-library/papers/decision-making-and-machine-learning-algorithms-selection-with-artificial-intelligent-rule-based-expert-system/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haipeng Guo & William Hsu, 2007. "A machine learning approach to algorithm selection for $\mathcal{NP}$ -hard optimization problems: a case study on the MPE problem," Annals of Operations Research, Springer, vol. 156(1), pages 61-82, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Corne, David & Dhaenens, Clarisse & Jourdan, Laetitia, 2012. "Synergies between operations research and data mining: The emerging use of multi-objective approaches," European Journal of Operational Research, Elsevier, vol. 221(3), pages 469-479.
    2. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:9:y:2022:i:4:p:54-60. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://www.rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.