Using a case-based reasoning approach for trading in sports betting markets

Alberola, Juan M.; García-Fornes, Ana

doi:10.1007/s10489-012-0381-9

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…As a result, the decision on how students should be allocated to supervisors is arduous, time consuming, and, quite often, stressful. This decision has almost countless possibilities and, making an optimal decision on the matter, may be unfeasible for human decision makers Despite this problem, Artificial Intelligence has proved to be a helpful tool in supporting decision making in a variety of complex domains such as emergency response [4], analytics [5], health [6], sports [7], or even education [8,9,10]. Its ability to manage complex decision problems (i.e., involving many variables), provide time effective responses, and learn from experience has put artificial intelligence to the forefront of today's ICT solutions.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Tools for Academic Management: Assigning Students to Academic Supervisors

Sánchez-Anguix¹,

Chalumuri²,

Alberola³

et al. 2020

INTED Proceedings

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In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Tools for Academic Management: Assigning Students to Academic Supervisors

Sánchez-Anguix¹,

Chalumuri²,

Alberola³

et al. 2020

INTED Proceedings

View full text Add to dashboard Cite

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Evaluating the effectiveness of machine learning models for performance forecasting in basketball: a comparative study

Papageorgiou,

Sarlis,

Tjortjis

2024

Knowl Inf Syst

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Sports analytics (SA) incorporate machine learning (ML) techniques and models for performance prediction. Researchers have previously evaluated ML models applied on a variety of basketball statistics. This paper aims to benchmark the forecasting performance of 14 ML models, based on 18 advanced basketball statistics and key performance indicators (KPIs). The models were applied on a filtered pool of 90 high-performance players. This study developed individual forecasting scenarios per player and experimented using all 14 models. The models’ performance ranking was developed using a bespoke evaluation metric, called weighted average percentage error (WAPE), formulated from the weighted mean absolute percentage error (MAPE) evaluation results of each forecasted statistic and model. Moreover, we employed a comprehensive forecasting approach to improve KPI's results. Results showed that Tree-based models, namely Extra Trees, Random Forest, and Decision Tree, are the best performers in most of the forecasted performance indicators, with the best performance achieved by Extra Trees with a WAPE of 34.14%. In conclusion, we achieved a 3.6% MAPE improvement for the selected KPI with our approach on unseen data.

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