Improving SDG Classification Precision of Topic Models with Combinatorial Fusion Algorithm

Orazbek, Ilyas; LaFleur, Marcelo; Hsu, David

doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00091

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…CFA is a relatively novel approach in predictive modeling, particularly in sports analytics. It offers a way to integrate predictions from multiple models, potentially capturing a more comprehensive picture than any single model could [24]. While its application in sports prediction is still emerging, CFA has been successfully employed in other domains like medical diagnosis and financial forecasting, where it has improved prediction accuracy by effectively combining diverse model outputs [24,25].…”

Section: Cfa In Predictive Modelingmentioning

confidence: 99%

Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Alfatemi,

Rahouti,

Hsu

et al. 2023

Preprint

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This paper presents a novel methodology for accurately predicting game outcomes in the NCAA Men's Basketball Tournament, known as March Madness. The high variance inherent to this tournament poses challenges that often exceed traditional forecasting methods. We implement a rigorous data preprocessing and feature engineering pipeline tailored to this problem using a historical NCAA Men's Tournament dataset. Four diverse neural network architectures, including convolutional, recurrent, feedforward, and residual networks, are developed to model team statistics and sequential game information. We formulate a robust forecasting approach by leveraging combinatorial fusion analysis (CFA) to merge predictions from these heterogeneous models. Our methodology synergistically integrates deep learning, CFA techniques, and domain-specific feature engineering to push the boundaries of state-of-the-art March Madness forecasting. This enables basketball enthusiasts to gain strategic advantages in tournament contests while advancing sports outcome prediction research.

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Section: Cfa In Predictive Modelingmentioning

confidence: 99%

Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Alfatemi,

Rahouti,

Hsu

et al. 2023

Preprint

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Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Alfatemi,

Rahouti,

Hsu

et al. 2024

Lecture Notes in Networks and Systems

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Improving SDG Classification Precision Using Combinatorial Fusion

Hsu

LaFleur

Orazbek

2022

Sensors

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Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). When measuring the relevance of a publication or document with respect to the 17 Sustainable Development Goals (SDGs) of the United Nations, a classification scheme is used. However, this classification process is a challenging task due to the overlapping goals and contextual differences of those diverse SDGs. In this paper, we use CFA to combine a topic model classifier (Model A) and a semantic link classifier (Model B) to improve the precision of the classification process. We characterize and analyze each of the individual models using the RSC function and CD between Models A and B. We evaluate the classification results from combining the models using a score combination and a rank combination, when compared to the results obtained from human experts. In summary, we demonstrate that the combination of Models A and B can improve classification precision only if these individual models perform well and are diverse.

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Improving SDG Classification Precision of Topic Models with Combinatorial Fusion Algorithm

Cited by 3 publications

References 10 publications

Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Advancing NCAA March Madness Forecasts Through Deep Learning and Combinatorial Fusion Analysis

Improving SDG Classification Precision Using Combinatorial Fusion

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