The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Automated recommendation of machine learning algorithms is receiving a large deal of
attention, not only because they can recommend the most suitable algorithms for a new task …

Meta-learning, or learning to learn, is a machine learning approach that utilizes prior
learning experiences to expedite the learning process on unseen tasks. As a data-driven …

A growing number of research papers shed light on automated machine learning (AutoML)
frameworks, which are becoming a promising solution for building complex machine …

This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's
algorithm for fast k-means clustering. To do so, we analyze the pruning mechanisms of …

Hyperparameter optimization plays a significant role in the overall performance of machine
learning algorithms. However, the computational cost of algorithm evaluation can be …

Efficient clustering algorithms, such as k-Means, are often used in practice because they
scale well for large datasets. However, they are only able to detect simple data …

Automated Feature Engineering (AFE) refers to automatically generate and select optimal
feature sets for downstream tasks, which has achieved great success in real-world …

A proper form of data characterization can guide the process of learning-algorithm selection
and model-performance estimation. The field of meta-learning has provided a rich body of …

A text mining characterization is proposed consisting of a set of meta-features, unlike
previous meta-learning approaches, some of them are extracted directly from raw text. Such …