Text Classification pipelines are a sequence of tasks needed to be performed to classify
documents into a set of predefined categories. The pre-processing phase (before training) of …

The ongoing COVID-19 pandemic has posed serious threats to the world population,
affecting over 219 countries with a staggering impact of over 162 million cases and 3.36 …

This paper introduces an evolutionary model in the scope of automated machine learning.
This model is in charge of learning hyper-heuristics that represent selection rules of the form …

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 …

Recent progress in AutoML has lead to state-of-the-art methods (eg, AutoSKLearn) that can
be readily used by non-experts to approach any supervised learning problem. Whereas …

Non-experts in Machine Learning research have an increasing demand for easy-to-use
methods to model solutions that use the large amounts of data available today, where such …

Although numerous open-source tools exist for machine learning with tabular data, there is a
scarcity of comparable resources tailored specifically for NLP. The lack of transparency in …

This paper introduces an evolutionary model in the scope of automated machine learning.
This model is in charge of learning hyper-heuristics that represent selection rules of the form …

In this paper, an evolutionary model, in the scope of automated machine learning, that
learns selection hyper-heuristics for text classification is presented. A hyper-heuristic is a set …

In this paper, an evolutionary model, in the scope of automated machine learning, that
learns selection hyper-heuristics for text classification is presented. A hyper-heuristic is a set …