Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Benchmarking models via classical simulations is one of the main ways to judge ideas in
quantum machine learning before noise-free hardware is available. However, the huge …

For many machine learning algorithms, predictive performance is critically affected by the
hyperparameter values used to train them. However, tuning these hyperparameters can …

In this paper, we present a context-based meta-reinforcement learning approach to tackle
the challenging data-inefficiency problem of Hyperparameter Optimization (HPO) …

Over the last decade, the long-running endeavour to automate high-level processes in
machine learning (ML) has risen to mainstream prominence, stimulated by advances in …

The problem's complexity assessment is an essential element of many topics in the
supervised learning domain. It plays a significant role in meta-learning–becoming the basis …

In recent years, many scholars have used data mining algorithms to discover the laws
related to the prevention of occupational injuries in the construction industry. Using accident …

Imbalanced problems can arise in different real-world situations, and to address this, certain
strategies in the form of resampling or balancing algorithms are proposed. This issue has …