Today, the use of social networks is growing ceaselessly and rapidly. More alarming is the
fact that these networks have become a substantial pool for unstructured data that belong to …

The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

Feature engineering is a crucial step in the process of predictive modeling. It involves the
transformation of given feature space, typically using mathematical functions, with the …

Feature construction and feature selection are two common pre-processing methods for
classification. Genetic Programming (GP) can be used to solve feature construction and …

Feature construction is an effort to transform the input space of classification problems in
order to improve the classification performance. Feature construction is particularly important …

Feature selection (FS) refers to the problem of selecting those input attributes that are most
predictive of a given outcome; a problem encountered in many areas such as machine …

Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …

Feature engineering is a crucial step for developing effective machine learning models.
Traditionally, feature engineering is performed manually, which requires much domain …

Computer-aided approaches have been widely used in pharmaceutical research to improve
the efficiency of the drug discovery and development pipeline. To identify and design small …

We present SLUG, a method that uses genetic algorithms as a wrapper for genetic
programming (GP), to perform feature selection while inducing models. This method is first …