Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Fraud exists in all walks of life and detecting and preventing fraud represents an important
research question relevant to many stakeholders in society. With the rise in big data and …

Default user detection plays one of the backbones in credit risk forecasting and
management. It aims at, given a set of corresponding features, eg, patterns extracted from …

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers.
Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for …

Trustable explanations of machine learning (ML) models are vital in high-risk uses of
artificial intelligence (AI). Apart from the computation of trustable explanations, a number of …

Logistic Regression (LR) is the most widely used machine learning model in industry for its
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …

Apart from being relied upon for feeding the entire world, the agricultural sector is also
responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority …