Evolutionary machine learning: A survey
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
[HTML][HTML] Application and theory gaps during the rise of artificial intelligence in education
Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the
absence of a comprehensive review on it, this research aims to conduct a comprehensive …
absence of a comprehensive review on it, this research aims to conduct a comprehensive …
A survey of predictive modeling on imbalanced domains
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
A survey of evolutionary computation for association rule mining
Abstract Association Rule Mining (ARM) is a significant task for discovering frequent patterns
in data mining. It has achieved great success in a plethora of applications such as market …
in data mining. It has achieved great success in a plethora of applications such as market …
Improved monarch butterfly optimization for unconstrained global search and neural network training
This work is a seminal attempt to address the drawbacks of the recently proposed monarch
butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence …
butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence …
A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data
Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items
frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets …
frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets …
Interpretable multiview early warning system adapted to underrepresented student populations
A Cano, JD Leonard - IEEE Transactions on Learning …, 2019 - ieeexplore.ieee.org
Early warning systems have been progressively implemented in higher education
institutions to predict student performance. However, they usually fail at effectively …
institutions to predict student performance. However, they usually fail at effectively …
Educational data mining methods: A survey
A Aleem, MM Gore - 2020 ieee 9th international conference on …, 2020 - ieeexplore.ieee.org
Educational Data Mining (EDM) is an emerging inter-disciplinary research area that involves
education and computer science. EDM employs data mining tools and techniques, on large …
education and computer science. EDM employs data mining tools and techniques, on large …
A virtual learning architecture enhanced by fog computing and big data streams
R Pecori - Future Internet, 2018 - mdpi.com
In recent years, virtual learning environments are gaining more and more momentum,
considering both the technologies deployed in their support and the sheer number of …
considering both the technologies deployed in their support and the sheer number of …
FR-Tree: A novel rare association rule for big data problem
In some situations, finding the rare association rule is of higher importance than the frequent
itemset. Unique rules represent rare cases, activities, or events in real-world applications. It …
itemset. Unique rules represent rare cases, activities, or events in real-world applications. It …