Feature selection

…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.

…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

Background: Diagnosing Attention Deficit Hyperactivity Disorder (ADHD) is a challenging process currently focused on questionnaires and clinical interviews, which could be sensitive to subjective biases (Farone et al. 2005). Some studies tried machine learning (ML) algorithms with neurophysiological data such as functional magnetic resonance imaging or electroencephalography to explore objective measures for diagnosing purporses (Mikolas et al. 2022). In this study, ML algorithms are built using cheaper and simpler measures including cognitive and intelligence assessments, reducing time and re

…challenge due to the limited sample-to-feature ratio. In this study, we aimed to improve the generalizability of brain-behavior associations in resting-state FC by focusing on diverse and stable edges, i.e., edges that show both high between-subject and low within-subject variability. We used resting-state data from 1003 participants with multiple fMRI sessions from the Human Connectome Project to gro

Gating represents multiplicative interactions in neural systems, and are well known motifs of biological neural circuits. Recently neural networks with gating have attracted increased attention. Gated Linear Networks (GLNs) have been studied for their interesting capabilities such as biologically plausible learning and reduced forgetting in sequential learning. The network has also been shown to mimic some aspects of cerebellar architecture and plasticity. However, there has been virtually no theoretical understanding of the capabilities and limitations of GLNs. To close this gap, we introduce

…We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuros