Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

Lopez-Bernal, Diego; Balderas, David; Ponce, Pedro; Molina, Arturo

doi:10.3390/fi13080193

Cited by 26 publications

(12 citation statements)

References 50 publications

(50 reference statements)

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“…There are some similar studies to ours but using a different dataset from the Wisconsin Breast Cancer database [22][23][24]. Using a larger dataset with more features, these studies have shown the feasibility of machine learning based models in predicting breast cancer risks in the healthcare domains.…”

Section: Background and Related Workmentioning

confidence: 79%

An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

Tran

Shi

2022

PLoS ONE

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Early detection of breast cancer plays a critical role in successful treatment that saves thousands of lives of patients every year. Despite massive clinical data have been collected and stored by healthcare organizations, only a small portion of the data has been used to support decision-making for treatments. In this study, we proposed an engineered up-sampling method (ENUS) for handling imbalanced data to improve predictive performance of machine learning models. Our experiment results showed that when the ratio of the minority to the majority class is less than 20%, training models with ENUS improved the balanced accuracy 3.74%, sensitivity 8.36% and F1 score 3.83%. Our study also identified that XGBoost Tree (XGBTree) using ENUS achieved the best performance with an average balanced accuracy of 97.47% (min = 93%, max = 100%), sensitivity of 97.88% (min = 89% and max = 100%), and F1 score of 96.20% (min = 89.5%, max = 100%) in the validation dataset. Furthermore, our ensemble algorithm identified Cell_Shape and Nuclei as the most important attributes in predicting breast cancer. The finding re-affirms the previous knowledge of the relationship between Cell_Shape, Nuclei, and the grades of breast cancer using a data-driven approach. Finally, our experiment showed that Random Forest and Neural Network models had the least training time. Our study provided a comprehensive comparison of a wide range of machine learning methods in predicting breast cancer risk. It can be used as a tool for healthcare practitioners to effectively detect and treat breast cancer.

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Section: Background and Related Workmentioning

confidence: 79%

An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

Tran

Shi

2022

PLoS ONE

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“…In education, the convergence of these technologies is often referred to as “Education 4.0,” not only where predictive models of student achievement may be constructed but also where teaching and learning approaches may be optimized. Lopez-Bernal et al (2021) caution, however, technologies need to complement human sensemaking and problem-solving:…”

Section: Resultsmentioning

confidence: 99%

Global trends in disruptive technological change: social and policy implications for education

Moravec,

Martínez-Bravo

2023

OTH

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Purpose The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education. Design/methodology/approach The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves a systematized literature review for data identification and collation adhering to defined selection criteria, and a network analysis to scrutinize data, consolidate information and unveil correlations and patterns from the literature review to produce a set of recommendations. Findings The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability toward future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified. Practical implications The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies. Originality/value Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies and four scenarios for policymakers and local communities to consider, enabling them to plot courses for their optimal futures.

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“…This study discusses the application of different algorithms to solve three different binary classification problems using three different datasets. Comparisons are made in specific case studies and their performance; it is proved that the optimized performance is the best [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

Geng¹,

Yan

2022

Computational Intelligence and Neuroscience

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This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

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Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

Cited by 26 publications

References 50 publications

An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

Global trends in disruptive technological change: social and policy implications for education

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning

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