Unbalanced classification has attracted widespread interest because of its broad applications. However, due to mainly the uneven class distribution, constructed classifiers are usually biased towar…
Human intelligence can simultaneously process many tasks with the ability to accumulate and reuse knowledge. Recent advances in artificial intelligence, such as transfer, multitask, and layered lea…
Extracting effective features from images is crucial for image classification, but it is challenging due to high variations across images. Genetic programming (GP) has become a promising machine-le…
Explainable artificial intelligence (XAI) has received great interest in the recent decade, due to its importance in critical application domains, such as self-driving cars, law, and healthcare. Ge…
Feature selection is to reduce both the dimensionality of data and the classification error rate (i.e., increase the classification accuracy) of a learning algorithm. The two objectives are often c…
The uncertain capacitated arc routing problem (UCARP) is an NP-hard combinatorial optimization problem with a wide range of applications in logistics domains. Genetic programming (GP) hyper-heurist…
Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among differ…
The blossoming of electric vehicles gives rise to a new vehicle routing problem (VRP) called capacitated electric VRP. Since charging is not as convenient as refueling, both the service of customer…
Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyze and understand th…
Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection i…