Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies and …
Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost …
It is challenging to develop accurate models for heavy construction equipment residual
value prediction using conventional approaches. This article proposes three Machine …
value prediction using conventional approaches. This article proposes three Machine …
Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …
researchers can employ several statistical rates, accordingly to the goal of the experiment …
Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying
their performance. Statistical comparisons are also a crucial part which allows for reliable …
their performance. Statistical comparisons are also a crucial part which allows for reliable …
[PDF][PDF] Four principles of explainable artificial intelligence
We introduce four principles for explainable artificial intelligence (AI) that comprise
fundamental properties for explainable AI systems. We propose that explainable AI systems …
fundamental properties for explainable AI systems. We propose that explainable AI systems …
Application of artificial intelligence to gastroenterology and hepatology
Since 2010, substantial progress has been made in artificial intelligence (AI) and its
application to medicine. AI is explored in gastroenterology for endoscopic analysis of …
application to medicine. AI is explored in gastroenterology for endoscopic analysis of …
EEG-based BCI emotion recognition: A survey
EP Torres, EA Torres, M Hernández-Álvarez, SG Yoo - Sensors, 2020 - mdpi.com
Affecting computing is an artificial intelligence area of study that recognizes, interprets,
processes, and simulates human affects. The user's emotional states can be sensed through …
processes, and simulates human affects. The user's emotional states can be sensed through …
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks
on a network. The success of a NIDS depends on the success of its algorithm and the …
on a network. The success of a NIDS depends on the success of its algorithm and the …