Integrating Feature Selection and Multiclass Classification for Sport Result Prediction
Lydia D Isaac1, I. Janani2
1Lydia D. Isaac, Department of Information Technology, Sona College of Technology, Salem, India.
2Janani. I, Department of Information Technology, Sona College of Technology, Salem, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1339-1342 | Volume-9 Issue-1, November 2019. | Retrieval Number: L37571081219/2019©BEIESP | DOI: 10.35940/ijitee.L3757.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning (ML) has become the most predominant methodology that shows good results in the classification and prediction domains. Predictive systems are being employed to predict events and its results in almost every walk of life. The field of prediction in sports is gaining importance as there is a huge community of betters and sports fans. Moreover team owners and club managers are struggling for Machine learning models that could be used for formulating strategies to win matches. Numerous factors such as results of previous matches, indicators of player performance and opponent information are required to build these models. This paper provides an analysis of such key models focusing on application of machine learning algorithms to sport result prediction. The results obtained helped us to elucidate the best combination of feature selection and classification algorithms that render maximum accuracy in sport result prediction.
Keywords: Machine Learning, Prediction, Supervised learning, Classification, Feature Selection, Sport Result Prediction
Scope of the Article: Classification