Topic: feature selection

Daniel Rodriguez examines how the structure of tabular datasets affects classification performance and the stability of feature selection, using software-defect prediction as a case study. The work evaluates data-complexity metrics across multiple algorithms to understand the effects of dimensionality, class overlap, and network structure. These relationships inform the choice of predictive models and feature-selection strategies suited to particular datasets, with the aim of producing more reliable and interpretable defect-prediction systems.

The application of neuroimaging methods to marketing has recently gained lots of attention. In analyzing consumer behaviors, the inclusion of neuroimaging tools and methods is improving our understanding of consumer’s preferences. Human emotions play a significant role in decision making and critical thinking. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuros

The natural world is full of noise, but the brain’s capacity for information transmission is severely limited. Therefore, discarding irrelevant information contained in sensory inputs while retaining salient features that are related to the input label, is key to survival. What are the salient features? And what are the underlying feature selection mechanisms? It is thought that the brain may implement information bottlenecks, which aim to optimise the trade-off between compression and preservation of salient information. However, information bottlenecks are notoriously difficult to implemen